WO2007051397A1

WO2007051397A1 - An information retrieval system and information retrieval method

Info

Publication number: WO2007051397A1
Application number: PCT/CN2006/002804
Authority: WO
Inventors: Wei Wang
Original assignee: Huawei Technologies Co., Ltd.
Priority date: 2005-11-01
Filing date: 2006-10-20
Publication date: 2007-05-10
Also published as: CN1858733B; CN1858733A

Abstract

An information retrieval system, which includes a search engine, a content indexing database provided to the search engine to perform the search, a user character database, and a content analysis system, is disclosed. An information retrieval method that includes the following steps is also provided correspondingly. The characteristic behavior information corresponding to the user identity in various time interval are stored, and the retrieval keywords input by the user are obtained, the search engine performs the retrieval base on the keyword to obtain the original retrieval result; the user identity and the current time information are obtained, and based on them the characteristic behavior data keyword of the corresponding user is retrieved; and the second retrieve is performed on the original retrieval result retrieved by the search engine based on the characteristic behavior information, and the retrieval results containing the keyword is displayed to the user at a first priority. And another information retrieval method and information retrieval system are provided. The information retrieval system can filter the user’s search according to the different characteristic behavior of the user, improve the accuracy and the performance of the user’s search on the associated information.

Description

Information retrieval system and retrieval method

The invention relates to the field of information retrieval technology, in particular to an information retrieval system and a retrieval method. Background of the invention

A search engine is a system that can obtain web page information, build a database, and provide queries. Depending on how you work, you can divide your search engine into two basic categories: the FullText Search Engine and the Directory.

The full-text search engine database relies on a software called "Spider" or "Crawlers" to automatically retrieve a large amount of web page content through various links on the network, and analyze and organize according to the rules. Forming. Both Google and Baidu are typical full-text search engine systems. A query to a full-text search engine is often referred to as a search for "all sites" or "all sites", such as Google's full-text search ( http:〃 www.google.com/intl/zh-CN/ ).

The catalogue is a manual database that collects and organizes website data, such as Yahoo China and Sohu, Sina, and NetEase. In addition, some navigation sites on the Internet can also be attributed to the original category, such as "Website Home" (http://www.haol23.com/). The query for the category directory is usually called the search "category directory" or search for "category website", such as "Sina search" (http://dir.sina.com.cn/) and "Yahoo China search" (http:/ /cn.search.yahoo.com/dirsrch/ ).

Full-text search engines and catalogs are used in different lengths. The full-text search engine relies on software, so the capacity of the database is very large, but its query results are often not accurate enough. The catalogue relies on manual collection and organization of websites, which can provide more accurate query results, but the collected content is very limited. . In order to complement each other, many search engines now offer both types of queries. The integration of these two types of search engines has also produced other search services, here, We also call them search engines, mainly in the following two categories:

1, meta search engine (META Search Engine)., these search engines generally do not have their own network robots and databases, their search results are by calling, controlling and optimizing the search results of other independent search engines and in a uniform format Displayed in the same interface. Although the meta search engine does not have "web robot" or "web spider" and does not have an independent index database, it has its own unique meta-search technology in terms of search request submission, retrieval interface proxy and search result display. For example, "metaFisher meta search engine" (http:AVww.hsfz.net/fish/ ), it calls and integrates data from multiple search engines such as Google, Yahoo AlltheWeb, Baidu and OpenFind.

2. Integrated Search Engine (All - in - One Search Page). The integrated search engine uses web technology to link multiple independent search engines on a web page. When querying, click or specify a search engine, input once, multiple search engines simultaneously query, and search results are displayed by different search engines by different pages. Such as "Internet Swiss Army Knife, (http: 〃 free.okey.net/%7Efree/searchl.htm).

Here again introduces the working principle of the search engine. The full-text search engine "web robot" or "web spider" is a kind of software on the network. It traverses the web space and can scan websites within a certain IP address range and along the network. The link on the page is from one page to another, from one website to another. In order to ensure the latest information collected, it will also return to the pages that have been captured. Web pages collected by web robots or web spiders must be analyzed by other programs. A large number of calculations are performed according to a certain correlation algorithm to create a web page index, which is added to the content index database. The full-text search engine that we usually see is actually a search interface of a search engine system. When a keyword is entered for query, the search engine finds an index of all relevant web pages that match the keyword from the huge content index database. And presented to us according to certain ranking rules. Different search engines, different content index databases, and different ranking rules, so when we use different search engines to query with the same keyword, the search results are not the same.

Conventional search engines now automatically access websites through software and track their hypertext links in turn and extract each file encountered in it through so-called "keywords" and mark each file in a large database. For later access. Specifically, with this type of extraction, such files are reduced, all semantic and syntactic information is extracted, but words containing content in the file are also included. These content words may exist within the file itself or only in the description section of the file's Hypertext Markup Language (HTML). In either case, the engine creates an entry for each such file, a file record. For each file, its content words are marked in a searchable data structure with a link back to the file record. The file record usually contains: a. A URL, which is a URL (Uniform Resource Locator, through which a web browser can access the corresponding file); b. Different content words in the file and in some engines The relative address of each such content word related to the other content words of the file; c. A segment summary of the file, usually only a few lines or the first few lines of the file; d, may be provided in its HTML description section a description of the file.

When a user uses a search engine, the user is provided with a keyword-based query that attempts to find a file containing as many keywords as possible, and when requested, according to an operator or other specification (eg, a logical operation, such as: Look for the range with / or / not). For each such file it finds, the engine retrieves its file record and sorts by the number of key matches in the file relative to other such files to provide the record to the user.

At present, the search engine simply responds to the keyword query provided by the user, and the user may have different behavior habits at different times and on different machines, thus having different needs, and the content information that the user wants to retrieve may have Different, but existing search methods do not consider these situations to classify search engine search results. Summary of the invention

In view of this, the present invention provides an information retrieval system and method.

An embodiment of the present invention provides an information retrieval system, including: a search engine, a content index database provided to a search engine for searching, and the following:

a user feature database that stores feature behavior information that the user has in different time periods; a content analysis system that respectively associates with the user feature database, the search engine, and the connected user The communication network of the terminal is connected, and is used for determining the current time and receiving the user identifier transmitted by the user terminal, and querying the user feature database to obtain the characteristic behavior information of the current time of the user identifier; and searching by the search engine. The result information is re-searched and sorted according to the obtained feature behavior information, and the retrieved search result is sent to the user terminal for display.

An embodiment of the present invention further provides an information retrieval method, which includes the following steps: saving feature behavior information corresponding to a user in different time periods;

Obtaining a search keyword input by the user, and the search engine searches for the original search result according to the search keyword;

Obtaining the user identifier and the current time, and retrieving the corresponding feature behavior information according to the obtained user identifier and the current time;

The original search result searched by the search engine is secondarily searched according to the feature behavior information, and the search result including the feature behavior information is displayed to the user.

An embodiment of the present invention further provides an information retrieval method, which is characterized by comprising the following steps:

The characteristic behavior information corresponding to the user under different conditions is saved, and the original retrieval is performed according to the retrieval keyword input by the user;

Obtain a user ID and a condition identifier;

Retrieving corresponding feature behavior information by using the user identifier and the condition identifier;

The original retrieval result is subjected to a second retrieval according to the characteristic behavior information.

Wherein, the condition is identified as time, machine model or a combination of the two.

The embodiment of the present invention further provides an information retrieval system, which includes an interconnected search engine and a content index database provided for searching by the search engine, and further includes:

a user feature database, which stores feature behavior information of the user under different conditions; a content analysis system, which is respectively connected with a user feature database, a search engine, and a communication network connected to the user terminal, for acquiring the current condition identifier and the slave Transmitted by the user terminal User identifier, and according to the condition identifier and the user identifier, querying the user feature database to obtain the feature behavior information of the user identifier under the corresponding condition; and performing the search result information searched by the search engine based on the obtained feature behavior information The sorting is searched again, and the sorted search result is again retrieved and sent to the user terminal for display.

Wherein in the system, the conditional identification can be time, or a machine model or a combination of the two.

It can be seen from the above method that the solution provided by the embodiment of the present invention can search for the original collection result searched by the search engine according to the keyword input by the user according to the time characteristic and the personalized characteristic behavior of the user corresponding to the machine model. The record is subjected to secondary screening and filtering, and the file record information that the user is really interested in is preferentially displayed to the user, which improves the accuracy and search efficiency of the user to retrieve relevant information. BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 shows the structure of a traditional search engine system.

2 is a system frame diagram of an information retrieval system according to an embodiment of the present invention.

FIG. 3 is a framework diagram of a user feature database according to an embodiment of the present invention.

4 is a skeleton diagram of a content analysis system according to an embodiment of the present invention.

FIG. 5 is a flowchart of a retrieval process according to an embodiment of the present invention. Mode for carrying out the invention

The traditional search engine system structure is shown in Figure 1:

"Web Spider" crawls web pages from the Internet, sends web pages to the "web database," and repeats the loop until all web pages are crawled.

The system obtains the text information from the "web database" and sends it to the "text index" module to create an index to form an "index database".

"User" by submitting a query request to the "query server", the server searches the relevant web pages in the "index database", sorts by relevance according to the "query server", and extracts The content summary of the keyword, the last page of the organization is returned to the "user".

One embodiment of the present invention considers that the user has different feature behavior information in different time periods. Therefore, after the search engine obtains the retrieval result, the retrieval result is processed according to the characteristic behavior information of the user corresponding to the current time period. The search result that meets the user characteristic behavior information is preferentially displayed to the user, thereby improving the accuracy of the search engine retrieval, and making the retrieval result provided to the user more close to the user's needs.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

First, Fig. 2 shows an information retrieval system according to an embodiment of the present invention, which includes a content analysis system 23, a user feature database 24, a search engine 22, and a content index database 21, wherein:

The content analysis system 23 is configured to receive the user identifier transmitted by the user terminal, the input search keyword, and the current time of obtaining the local server, and query the user feature database 24 to match the characteristic behavior of the user in the time period, and pass the search engine. The searched pages are retrieved and filtered again, so that the retrieved pages are presented to the user in the order of priority behavior of the feature behaviors exhibited by the user during the time period. In addition to the current time can be used as a conditional identification, the combination of the machine model, as well as the time and machine model, can also be used as conditional identification, which allows the information retrieval system to provide users with higher retrieval accuracy and search efficiency.

The user feature database 24 is used to store the characteristic behavior information of the user, especially the characteristic behavior information of the user in different time periods, and the database is described in detail later, and details are not described herein.

The search engine 22, which is a text and keyword based search tool, returns a list of required file pointers with a file title after searching in the existing content index database 21, and usually has some extracted from the body of the file. Descriptive text.

The content index database 21 automatically accesses the website by activating an automated program implemented by the software (such as "web spider") and sequentially tracks the hypertext connection therein and extracts each file encountered therein by a so-called "keyword", And stored in the database, provided to the search engine 22 for access.

FIG. 3 is an embodiment of the user feature database 24, which may be, but is not limited to, The preservation of the message. The personal user information table, the time period information table, the feature behavior table, and the matching table are described in detail below. Correspondingly, the feature behavior information corresponding to the machine model number, the time, the machine model, and the group cooperation condition identifier of the two are saved in the user feature database.

The personal user information table is used to store the personal information of the user, and may be a letter input when the user registers. Table 1 below shows a user information table:

Table 1

The time period information table is used to store different time period numbers corresponding to different time segments, and the time segment number is used to facilitate the retrieval of the database, and the setting of the time period is more flexible. As shown in the table below

2 shows a time period information table:

Table 2

Of course, the table when the machine model is used for conditional identification is a machine model information table, which is similar to the time zone information table and has two machine model numbers and machine models. The condition table when identifying the group cooperation conditions of time and machine model, here is an example (see Table 3 below), and its use and time period information Table 2 are similar, just one more.

table 3

The feature behavior table is used to store different feature lines corresponding to different feature behavior keywords of the user. For the number, one of the feature behavior keywords may also have a dependent keyword, which are all characteristic behavior information. A characteristic behavior table shown in Table 4 below:

Table 4

: The feature behavior number corresponding to the number. Through this table, the relationship between Table 1, Table 2 and Table 4 is established, that is, the relationship between different time periods and characteristic behavior keyword/characteristic behavior dependent keywords is established. Table 5 below shows a match table:

table 5

Each of the data in Table 5 above is a complete feature behavior information, which also includes a feature priority item, which is used to identify the priority of different feature behaviors of the user within a certain period of time. The example shown in Table 5 indicates that the user U001 has a feature priority level 9 of the feature behavior number C001 and a feature priority level 8 of the feature behavior number C002 in the time period T001, indicating that the user U001 is more in the time period T001. It is biased to exhibit characteristic behavior with feature behavior number C001.

The data stored in the user feature database 24 may be provided by a system for collecting user behavior characteristics. For the implementation of the system for collecting user behavior characteristics, refer to the system and method for collecting user service behavior features applied by the applicant. "Invented.

4 is a block diagram of the content analysis system 23, including a data unit 231, a search engine interface 232, an analysis unit 233, a retrieval analysis unit 234, and a retrieval data storage unit 235. In this embodiment, it is identified by time as a condition.

The data transceiver unit 231 is configured to implement interaction with the user terminal, and receive the user through the user terminal. The search keyword input is input to the search engine interface 232, and the obtained user identifier is sent to the analysis unit 233.

The search engine interface 232 is configured to implement interaction with the search engine 22, send search keywords sent by the data transceiving unit 231 to the search engine 22, and receive search results of the search engine 22 to the search data storage unit 235.

The search data storage unit 235: saves the search results of the search engine 22 sent from the search engine interface 232 for analysis by the search analysis unit 234.

The analyzing unit 233 is configured to receive the user identifier sent by the data transceiver unit 231 and obtain the current search time, where the current search time may be the time obtained by the system, or may be the time reported by the machine, and according to the user. The user search database 24 is identified and obtained by the current search time, and the feature behavior information corresponding to the user identifier and the search time is obtained and provided to the search analysis unit 234. The behavioral feature information may include, but is not limited to, a feature behavior keyword and a feature behavior dependent keyword. Accordingly, when the machine model is used as the conditional identification, the analysis unit 233 is a machine type analysis unit (not shown) which performs processing similar to that of the analysis unit 233, and correspondingly acquires and processes the machine model information. The analysis unit determines the corresponding machine model number (ie, the machine model identification) by the machine model reported by the machine, instead of the analysis unit 233, the current time can be obtained directly from the system side, or the current time can be obtained from the machine side, and then After obtaining the user identifier and the machine model identifier, the database 24 is retrieved to obtain corresponding feature behavior information. When the combination of time and machine model is used as the conditional identification, the corresponding steps and conditions are similar to the above steps and methods, except that the machine model is changed to the combination of time and machine model, so that the conditions become more specific.

The search analysis unit 234 is configured to receive the feature behavior keyword information sent by the analysis unit 233, and perform secondary search filtering and/or sorting on the search result stored in the search data storage unit 235, and filter and / or the sorted search result is sent to the data transceiving unit 231 to be returned to the user terminal for display to the user.

Referring now to FIG. 4, reference is also made to the information retrieval system of the embodiment of the present invention shown in FIG. The flow chart of the present search process, the information retrieval method of the present invention is described in detail, wherein the steps are

505, 506 are also an embodiment of a conditional identification based retrieval method of the present invention. This embodiment is identified by time as a condition and includes the following parts:

Step 501: First, the user inputs a search keyword in a search engine provided by the user terminal according to the information to be queried, and may input a Boolean type (for example, "and" or "or") between consecutive keywords or Operators that other search engines can recognize.

Assume that in this example, the user inputs a search keyword "game" at the user terminal, requesting to query related information.

Step 502: The information is transmitted to the content analysis system 23 through the network, and the data query unit 231 of the content analysis system 23 obtains the keyword information of the user query; and the data transceiver unit 231 also obtains the identifier of the user, and the user identifier can be obtained. It is input by the user through the user terminal, or may be entered when the user logs in when using the information retrieval system of the embodiment of the present invention.

Step 503: The data transceiving unit 231 sends the obtained keyword to the search engine interface 232, and sends the user identification information to the analyzing unit 233.

In this example, the data transceiving unit 231 sends the keyword "game" input by the user to the search engine interface 232; and the obtained identifier U001 of the user is sent to the analyzing unit 233.

Step 504: The search engine interface 232 sends the obtained keyword of the user query to the search engine 22, and the search engine 22 retrieves the related information in the content index database 21 according to the keyword, and returns the search result to the search engine interface 232, and then It is sent to the search data storage unit 235 for storage.

Step 505: The analyzing unit 233 finds the matching related feature behavior data from the user feature database 24 according to the obtained user identifier and current time information, and sends the matching characteristic behavior data to the retrieval analyzing unit 234. The time information may be provided by a local server loaded with the content analysis system or by any computer device within the network, preferably provided by a local server.

Correspondingly, when the combination of the machine model and the time, the machine model can also be used as a condition, the analysis unit 233 is a machine type analysis unit, or a group having the above unit functions. In conjunction with units (not shown), these units perform similar processing as the analysis unit 233, correspondingly acquiring and processing information such as machine models. After obtaining the user identifier and the condition identifier (time, machine model or a combination of the two), the analysis unit searches the user feature database to obtain corresponding feature behavior information, and the retrieval process is:

And searching, by the user identifier, the intermediate feature behavior information of the user from the user feature database. The corresponding feature behavior information is retrieved from the intermediate feature behavior information of the user by using the condition identifier. The intermediate feature behavior information, the corresponding feature behavior information includes a feature behavior keyword and/or a feature behavior dependent keyword.

The two steps may be in no order, that is, the intermediate feature behavior information of the user may be retrieved from the user feature database by using the condition identifier, and then the user identifier is used to retrieve the intermediate feature behavior information from the user. Corresponding feature behavior information.

In this example, the corresponding time period number T001 is obtained according to the time information; and the user behavior preference and priority of the user at the moment are retrieved from the above table 5 of the user feature database 24 according to the user identifier U001 and the time period number (C001). , 9), ( C002, 8 )......; According to the above Table 4, the user's characteristic behavior keywords and characteristic behavior subordinate keywords at the moment are: games, video games, computer games...; music, Classical, orchestral... The user's characteristic behavior keywords and related feature priorities are sent to the retrieval analysis unit 234.

Correspondingly, the corresponding model number (ie, the model number of the machine) can also be obtained from the database 24 according to the model information reported by the machine; and the user is retrieved from the table of the database 24 according to the user identifier and the model number of the machine. User behavior preferences and priorities; these feature behavior keywords and related feature priorities of the user are then sent to the retrieval analysis unit 234. These steps are similar to the steps described above with time as a conditional identification. The combination of machine identification and time is similar to the above steps.

The following describes the intermediate feature behavior information, and the corresponding feature behavior information.

Before the feature information is obtained, the user ID must be saved in the database under different conditions. Corresponding feature behavior information. The condition identification can be a combination of time, machine model or time and machine model.

For example, match table 5, which shows individual feature behavior information. Searching the user characteristic information matching table in the database by using a specific user number (that is, the user identifier), and the obtained characteristic behavior information is the intermediate characteristic behavior information, and then according to the time period number (ie, the condition Identification) re-searching the result of the last search, and retrieving characteristic behavior information that meets a certain time period (ie, the described piece, which may also be a combination of a machine model or a time and a machine model), that is, the corresponding Feature behavior information. Of course, the intermediate feature behavior information may be retrieved according to the time segment number (ie, the condition), and then the corresponding feature is obtained from the intermediate feature behavior information according to the user number (ie, the user identifier). Behavioral information.

When the condition is the combination of the machine model or time and the machine model, the step of retrieving the corresponding characteristic behavior information is similar to the step when the time is the time, only the conditional identification has changed.

Step 506: The retrieval analysis unit 234 obtains the relevant retrieval result (such as page information) that the user has searched from the retrieval data storage unit 235 by using the user identifier, and then performs the secondary search by the received feature behavior keyword and related feature priority. The result information is retrieved and reordered, so that the page information that the user really has is first displayed to the user.

In this example, when the search result is subjected to the secondary search sorting, the feature behavior keyword (game, electronic game, computer game...) with high priority is first searched, and the retrieved file is retrieved. The information is listed first; then the low-priority feature behavior keywords (music, classical, orchestral...) are searched, and the retrieved file information is listed later; then the feature is not included in the secondary search. The information of the original search result of the behavior keyword is listed last. The retrieval process for these keywords is not described in detail in the embodiments of the present invention, and these techniques are included in each text retrieval system.

Step 507: The search analyzing unit 234 sends the search result sorted by the secondary search to the data transceiving unit 231, and the data transceiving unit 231 sends the result of the secondary search sorting (such as page information) to the user terminal for display to the user. The above search scheme can be used in almost any information retrieval system to increase the search accuracy of the search engine, whether or not the engine is a regular engine. In addition, embodiments of the present invention also improve the accuracy of retrieving information from a mass database, regardless of the language in which the textual information is used, such as Chinese, English, French, German, and the like.

The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention. Any modifications, equivalents, improvements, etc., which are included in the spirit and scope of the present invention, should be included in the present invention. Within the scope of protection.

Claims

Claim

An information retrieval system, comprising: an interconnected search engine and a content index database provided for searching by a search engine, wherein:

The user feature database stores a feature behavior information analysis system that the user has in different time periods, and is respectively connected to the user feature database, the search engine, and the communication network connecting the user terminals, for determining the current time and receiving. The user identifier transmitted by the user terminal is used to query the user feature database to obtain the feature behavior information of the current time of the user identifier; and the search result information searched by the search engine is re-searched and sorted according to the obtained feature behavior information. The retrieved search result is again retrieved and sent to the user terminal for display.

2. The system according to claim 1, wherein the user feature database comprises:

a time period information table, configured to store different time period numbers corresponding to different time segments; and the dependent keyword information of the keyword and/or feature behavior;

The system according to claim 2, wherein the user feature database further comprises: a personal user information table for storing personal information of the user.

4. The system of claim 1 wherein the content analysis system comprises:

a data transceiving unit, configured to implement interaction with the user terminal, receive a search keyword input by the user terminal, and send the search keyword to the search engine interface, and send the user identifier to the analysis unit; the search engine interface is configured to send the data transceiver unit Search keywords are sent to the search engine, and search results received by the search engine are sent to the search data storage unit; Retrieving a data storage unit, configured to save search results of the search engine sent by the search engine interface, to be provided to the search analysis unit;

The analyzing unit is configured to receive the user identifier sent by the data transceiver unit and determine the current time, and retrieve the user feature database according to the method, and obtain the feature behavior information corresponding to the current time of the user identifier, and provide the information to the search analysis unit;

a search analysis unit, configured to receive feature behavior information sent by the analysis unit, and perform secondary search filtering and/or sorting on the search result stored in the search data storage unit, and filter and/or sort the The retrieval result is sent to the data transceiver unit for return to the user terminal.

5. An information retrieval method, comprising:

Saving feature behavior information corresponding to users in different time periods;

The original search result searched by the search engine is subjected to secondary search according to the feature behavior information, and the search result including the feature behavior information is displayed to the user.

The method according to claim 5, wherein the step of acquiring the user identifier comprises: receiving a user identifier input by the user through the user terminal; or receiving a user identifier entered when the user logs in to the system.

7. The method according to claim 5, wherein the step of acquiring the current time comprises: obtaining the current time provided from the local server or any computer device on the network.

The method according to claim 5, wherein the different feature behavior information is set with different priorities, and the performing the second retrieval further comprises:

Original search results searched by the search engine according to the different feature behavior information Secondary search;

The search results after the corresponding secondary search are sorted according to the priority of the feature behavior information.

The method according to claim 5, wherein the feature behavior information comprises: a feature behavior keyword and/or a feature behavior dependent keyword.

10. An information retrieval method, comprising:

Obtain a user ID and a condition identifier;

Retrieving corresponding feature behavior information by using the user identifier and the condition identifier; and performing a second retrieval on the original search result according to the feature behavior information.

1 1. The method according to claim 10, wherein the condition is identified as time, machine model or a combination of the two.

The method according to claim 10, wherein the retrieving the corresponding feature behavior information by using the user identifier and the condition identifier comprises:

Obtaining, by the user identifier, intermediate characteristic behavior information of the user;

Obtaining the corresponding feature behavior information from the intermediate feature behavior information of the user by using the condition identifier; or

Obtaining the intermediate feature behavior information of the user by using the condition identifier;

The corresponding feature behavior information is obtained from the intermediate feature behavior information of the user by using the user identifier.

13. An information retrieval system, comprising: an interconnected search engine and a content index database provided for searching by a search engine, wherein:

a user feature database, which stores feature behavior information of the user under different conditions; a content analysis system, which is respectively connected with a user feature database, a search engine, and a communication network connected to the user terminal, for acquiring the current condition identifier and the slave Transmitted by the user terminal User identifier, and according to the condition identifier and the user identifier, querying the user feature database to obtain the user identifier, the feature behavior information under the corresponding condition identifier; and performing the search result information searched by the search engine according to the obtained feature behavior information The sorting is searched again, and the sorted search result is again retrieved and sent to the user terminal for display.

14. The system according to claim 13, wherein the user feature database comprises:

a condition identification table, configured to store different condition identifiers and corresponding condition numbers;

Feature behavior table for storing different feature behaviors of users

Dependent keyword information for keywords and/or feature behavior;

Match table for storing different conditions of the user

The system according to claim 13, wherein the content analysis system comprises:

a data transceiving unit, configured to implement interaction with the user terminal, receive a search keyword input by the user terminal, and send the search keyword to the search engine interface, and send the user identifier to the analysis unit; the search engine interface is configured to send the data transceiver unit Search keywords are sent to the search engine, and search results received by the search engine are sent to the search data storage unit;

Retrieving a data storage unit for storing a search result of the search engine sent by the search engine interface for providing to the search analysis unit;

An analyzing unit, configured to receive a user identifier sent by the data transceiver unit, and obtain a condition identifier, and retrieve a user feature database according to the user identifier and the condition identifier, and obtain feature behavior information of the user identifier under the corresponding condition identifier, and provide the Retrieve the analysis unit;

a search analysis unit, configured to receive feature behavior information sent by the analysis unit, and perform secondary search filtering and/or ordering on the search result stored in the search data storage unit, and filter and/or sort the The retrieval result is sent to the data transceiver unit for return to the user terminal.

16. The system according to claim 13, wherein the condition identifier can be Time, or model of the machine, or a combination of the two.