CN113779032B - Search engine index construction method and device based on cyclic neural network - Google Patents
Search engine index construction method and device based on cyclic neural network Download PDFInfo
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Abstract
The invention discloses a search engine index construction method and device based on a cyclic neural network, which relate to the technical field of search engine index construction and aim at the problems of weak self-adaption capability, low data mining precision and poor stability and reliability of the existing search engine index construction method and device based on the cyclic neural network, and the scheme is provided in the present invention that the search engine index construction method based on the cyclic neural network comprises the following steps: s1, index acquisition: acquiring index content input by a user; s2, index analysis: matching the analyzer according to the index content, and analyzing the index content input by the user; s3, information searching: and extracting index keywords, and carrying out information search based on the cyclic neural network by combining the keywords. The invention has strong self-adaptive capacity, strong indexing performance, convenient accurate mining of index data and high stability and reliability.
Description
Technical Field
The invention relates to the technical field of search engine index construction, in particular to a search engine index construction method and device based on a cyclic neural network.
Background
The cyclic neural network is a structure which repeatedly happens along with the time, the search engine index is a system for automatically collecting information and providing the information for a user to inquire after processing, the accuracy of the search engine index is based on the construction of the search engine index, and the existing construction method and construction device of the search engine index still have the defects of weak self-adaption capability, low data mining accuracy and poor stability and reliability in the actual use process.
Disclosure of Invention
The invention provides a search engine index construction method and device based on a cyclic neural network, which solve the problems of weak self-adaptation capability, low data mining accuracy and poor stability and reliability of the existing search engine index construction method and device based on the cyclic neural network.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a search engine index construction method based on a cyclic neural network comprises the following steps:
s1, index acquisition: acquiring index content input by a user;
s2, index analysis: matching the analyzer according to the index content, and analyzing the index content input by the user;
s3, information searching: extracting index keywords, and carrying out information search based on a cyclic neural network by combining the keywords;
s4, information index: understanding and indexing the searched information, extracting index items from the information, and generating an index list;
s5, index feedback: and sequencing the index list according to the index relativity, and returning the index result.
The step S1 indexes the index content related to acquiring the user input, wherein the index content includes, but is not limited to, characters, words, phrases, articles, pictures and links.
And step S2, matching the analyzer according to the index content related to the index analysis, analyzing the index content input by the user, wherein the analyzer comprises, but is not limited to SQL SERVER, MYSQL and ORACLE, matching the content which is indexed according to the requirement input by the user in the actual operation process, and analyzing the content after matching.
The step S3 of information searching is related to extracting index keywords, the information searching is performed based on a cyclic neural network by combining the keywords, roaming is performed in the cyclic neural network, relevant information needing to be indexed is found and collected, and the method further comprises the steps of constructing the cyclic neural network before searching the information, and the construction method of the cyclic neural network comprises the following steps:
s31, collecting all data in a range of a cyclic neural network to be created;
s32, extracting concept words from the collected data, and collecting the extracted concept words;
s33, establishing a main relation between the concept words and the data;
s34, verifying and adjusting the relation between the concept words and the data.
And the step S4 is used for understanding and indexing the searched information related to the information index, extracting index items from the information, and generating an index list, wherein the step S3 is used for understanding the information searched in the information search, extracting the index items from the information search, and the index items are used for representing the documents and generating an index table of a document library.
And step S5, the index feedback is related to the step S5, the index results are sequenced according to the index relevance, and returned, the documents needing to be indexed are quickly searched in the index table indexed by the information in the step S4 according to the index content input by the user, the relevance evaluation is performed, the index results to be output are sequenced, and the query requirement of the user is installed to reasonably feed back the information of the index content.
A search engine index construction apparatus based on a recurrent neural network, the search engine index construction apparatus comprising an acquisition section, a processing section, and a retrieval section, the acquisition section comprising:
the interactive panel is connected with the user input module and the retrieval module and is used for providing an input platform for the book input module, receiving the index content retrieved by the retrieval module, displaying the index content and jumping to the user input module according to the input requirement;
the user input module is connected with the interactive panel and the analysis matching module and is used for receiving index skip of the interactive panel, then acquiring index content input by a user and transmitting the acquired index content to the analysis matching module;
the processing section includes:
the analysis matching module is connected with the user input module and the analysis module and is used for receiving index content input by a user and transmitted by the user input module, then carrying out matching selection on the analyzer according to the index content and transmitting a matching result and the index content to the analysis module;
the analysis module is connected with the analysis matching module and the keyword extraction module and is used for receiving the analysis matching result and the index content transmitted by the analysis matching module, then analyzing the index content based on the matched analysis and transmitting the analysis result to the keyword extraction module;
the keyword extraction module is connected with the analysis module and the search module and is used for receiving the analysis result transmitted by the analysis module, extracting keywords from the index content according to the analysis result and transmitting the extracted keywords to the search module;
the retrieval section includes:
the searching module is connected with the keyword refining module, the cyclic neural network and the indexing module, and is used for receiving the keywords refined by the keyword refining module, searching information based on the cyclic neural network by combining the keyword information, and transmitting the searched information to the indexing module;
the index module is connected with the search module and the retrieval module and is used for receiving the searched information transmitted by the search module, then understanding the searched information, extracting index items, generating an index library based on an index list and transmitting the index library to the retrieval module;
the retrieval module is connected with the index module and the interactive panel and is used for receiving the index library transmitted by the index module, sorting the index list of the index library according to the relativity of the index content and returning the index result to the interactive panel.
The beneficial effects of the invention are as follows:
the method has the advantages that the content to be indexed is subjected to self-adaptive analysis matching through the analysis matching module, various optimization strategies of the index are realized, performance is improved, accurate mining of index data is facilitated, meanwhile, stability and reliability of the index of a search engine are enhanced, information searching is conducted on the index based on a cyclic neural network, the searched information is understood through the index module, an index list is extracted from the information, relevance of the index list is arranged by combining index content, query and feedback are conducted through the interactive panel, query visualization is conducted, construction difficulty of the index is reduced, and stability of index construction is improved.
In summary, the search engine index construction method and device based on the cyclic neural network are high in self-adaption capability, index performance and stability and reliability, and are convenient for accurately mining index data.
Drawings
FIG. 1 is a flow chart of a search engine index construction method based on a recurrent neural network according to the present invention.
Fig. 2 is a block diagram of a search engine index constructing apparatus based on a recurrent neural network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 2, a search engine index constructing apparatus based on a recurrent neural network, the search engine index constructing apparatus including an acquisition section, a processing section, and a retrieval section, the acquisition section including:
the interactive panel is connected with the user input module and the retrieval module and is used for providing an input platform for the book input module, receiving the index content retrieved by the retrieval module, displaying the index content and jumping to the user input module according to the input requirement;
the user input module is connected with the interactive panel and the analysis matching module and is used for receiving index skip of the interactive panel, then acquiring index content input by a user and transmitting the acquired index content to the analysis matching module;
the processing section includes:
the analysis matching module is connected with the user input module and the analysis module and is used for receiving index content input by a user and transmitted by the user input module, then carrying out matching selection on the analyzer according to the index content and transmitting a matching result and the index content to the analysis module;
the analysis module is connected with the analysis matching module and the keyword extraction module and is used for receiving the analysis matching result and the index content transmitted by the analysis matching module, then analyzing the index content based on the matched analysis and transmitting the analysis result to the keyword extraction module;
the keyword extraction module is connected with the analysis module and the search module and is used for receiving the analysis result transmitted by the analysis module, extracting keywords from the index content according to the analysis result and transmitting the extracted keywords to the search module;
the retrieval section includes:
the searching module is connected with the keyword refining module, the cyclic neural network and the indexing module, and is used for receiving the keywords refined by the keyword refining module, searching information based on the cyclic neural network by combining the keyword information, and transmitting the searched information to the indexing module;
the index module is connected with the search module and the retrieval module and is used for receiving the searched information transmitted by the search module, then understanding the searched information, extracting index items, generating an index library based on an index list and transmitting the index library to the retrieval module;
the retrieval module is connected with the index module and the interactive panel and is used for receiving the index library transmitted by the index module, sorting the index list of the index library according to the relativity of the index content and returning the index result to the interactive panel.
Example 2
Referring to fig. 1, a search engine index construction method based on a recurrent neural network includes the steps of:
s1, index acquisition: acquiring index contents input by a user, wherein the index contents comprise, but are not limited to, characters, words, phrases, articles, pictures and links;
s2, index analysis: matching the analyzer according to the index content and analyzing the index content input by the user, wherein the analyzer comprises, but is not limited to SQL SERVER, MYSQL and ORACLE, matching the content which is indexed according to the input requirement of the user in the actual operation process, and analyzing the content after matching;
s3, information searching: extracting index keywords, carrying out information search based on a cyclic neural network by combining the keywords, roaming in the cyclic neural network, finding and collecting relevant information to be indexed, and constructing the cyclic neural network before searching the information, wherein the construction method of the cyclic neural network comprises the following steps:
s31, collecting all data in a range of a cyclic neural network to be created;
s32, extracting concept words from the collected data, and collecting the extracted concept words;
s33, establishing a main relation between the concept words and the data;
s34, verifying and adjusting the relation between the concept words and the data;
s4, information index: understanding and indexing the searched information, extracting index items from the information, and generating an index list, wherein the understanding step S3 is used for searching the searched information, extracting the index items from the information, and the index list is used for representing documents and generating a document library;
s5, index feedback: and (4) sorting the index list according to the index relativity, returning the index result, rapidly searching the documents needing to be indexed in the index list indexed by the information in the step (S4) according to the index content input by the user, evaluating the relativity, sorting the index result to be output, and reasonably feeding back the information of the index content by installing the query requirement of the user.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (4)
1. The search engine index construction method based on the cyclic neural network is characterized by comprising the following steps of:
s1, index acquisition: acquiring index content input by a user;
s2, index analysis: matching the analyzer according to the index content, and analyzing the index content input by the user;
s3, information searching: extracting index keywords, and carrying out information search based on a cyclic neural network by combining the keywords;
s4, information index: understanding and indexing the searched information, extracting index items from the information, and generating an index list;
s5, index feedback: ordering from the index list according to the index relativity, and returning the index result;
the step S1 is to index and acquire the index content input by a user, wherein the index content comprises, but is not limited to, characters, words, phrases, articles, pictures and links; the step S2 of index analysis refers to matching the analyzer according to the index content, and analyzing the index content input by the user, wherein the analyzer comprises, but is not limited to SQL SERVER, MYSQL and ORACLE, matching the content which is indexed according to the requirement input by the user in the actual operation process, and analyzing the content after matching; the step S3 is to refine index keywords related to information searching, and to search information based on a cyclic neural network by combining the keywords, roam in the cyclic neural network, and find and collect relevant information to be indexed; the step S3 of information searching is characterized in that extracted index keywords are related to information searching, the information searching is carried out based on a cyclic neural network by combining the keywords, and the method further comprises the steps of constructing the cyclic neural network before searching the information, wherein the construction method of the cyclic neural network comprises the following steps:
s31, collecting all data in a range of a cyclic neural network to be created;
s32, extracting concept words from the collected data, and collecting the extracted concept words;
s33, establishing a main relation between the concept words and the data;
s34, verifying and adjusting the relation between the concept words and the data;
the step S4 is used for understanding and indexing the searched information related to the information index, extracting index items from the information, and generating an index list, wherein the information searched in the step S3 is understood, the index items are extracted from the information, and the index list is used for representing documents and generating an index table of a document library;
and step S5, the index feedback is related to the step S5, the index results are sequenced according to the index relevance, and returned, the documents needing to be indexed are quickly searched in the index table indexed by the information in the step S4 according to the index content input by the user, the relevance evaluation is performed, the index results to be output are sequenced, and the query requirement of the user is installed to reasonably feed back the information of the index content.
2. A search engine index construction device based on a recurrent neural network applied to the above claim 1, the search engine index construction device comprising an acquisition section, a processing section, and a retrieval section, characterized in that the acquisition section comprises:
the interactive panel is connected with the user input module and the retrieval module and is used for providing an input platform for the book input module, receiving the index content retrieved by the retrieval module, displaying the index content and jumping to the user input module according to the input requirement;
the user input module is connected with the interactive panel and the analysis matching module and is used for receiving index skip of the interactive panel, then acquiring index content input by a user and transmitting the acquired index content to the analysis matching module.
3. The search engine index constructing apparatus based on a recurrent neural network as claimed in claim 2, wherein the processing section comprises:
the analysis matching module is connected with the user input module and the analysis module and is used for receiving index content input by a user and transmitted by the user input module, then carrying out matching selection on the analyzer according to the index content and transmitting a matching result and the index content to the analysis module;
the analysis module is connected with the analysis matching module and the keyword extraction module and is used for receiving the analysis matching result and the index content transmitted by the analysis matching module, then analyzing the index content based on the matched analysis and transmitting the analysis result to the keyword extraction module;
and the keyword extraction module is connected with the analysis module and the search module and is used for receiving the analysis result transmitted by the analysis module, extracting keywords from the index content according to the analysis result and transmitting the extracted keywords to the search module.
4. The search engine index constructing apparatus based on a recurrent neural network as claimed in claim 2, wherein the retrieving part comprises:
the searching module is connected with the keyword refining module, the cyclic neural network and the indexing module, and is used for receiving the keywords refined by the keyword refining module, searching information based on the cyclic neural network by combining the keyword information, and transmitting the searched information to the indexing module;
the index module is connected with the search module and the retrieval module and is used for receiving the searched information transmitted by the search module, then understanding the searched information, extracting index items, generating an index library based on an index list and transmitting the index library to the retrieval module;
the retrieval module is connected with the index module and the interactive panel and is used for receiving the index library transmitted by the index module, sorting the index list of the index library according to the relativity of the index content and returning the index result to the interactive panel.
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