TW202407575A - An editing system used for recommending articles - Google Patents

Abstract

The present invention provides an editing system for recommending articles, which includes a writing application program and a cloud server, the writing application program generates a draft of the article and transmits it to the cloud server. The aforementioned cloud server also contains a plurality of articles, and by separating the first draft of the article and the plurality of articles into a first draft word combination and a plurality of article word combinations, these combinations are converted into a first draft vector and a plurality of vectors respectively, so that the plurality of vectors correspond to the plurality of articles; the plurality of articles and the plurality of vectors are divided into multiple groups and a topic model is created based on said multiple groups, the first draft vector is imported into the topic model, a group is selected and the similarity value between a vector of the group and the first draft vector is calculated, when the similarity value is higher than a threshold, the article corresponding to the vector is recommended.

Description

An editing system for recommending articles

The present invention generally relates to an editing system, and in particular to an editing system for recommending articles.

Traditionally, when the author writes an article, he usually writes based on the content or experience he is familiar with in the past. In the process of writing, the content the author writes is often limited by his past reading direction. There is too much emphasis or insufficient discussion in the content. Even though the author spent time reading articles from many sources in order to enrich the content he wrote, there are too many sources of existing articles, making it impossible for the author to find articles that are closely related to the author's thinking in the limited time. Moreover, when browsing articles on certain website platforms, these The platform often provides other related content recommendations at the end of the article. The above recommended content is usually based on 2 to 3 articles as extended reading, and these articles as extended reading are sometimes not related to the subject content written by the author. Positive correlation.

On the other hand, when the author read multiple articles obtained through the search, the proportion and depth of each aspect of the topic to be discussed in each read article were different, making it difficult for the author to compare each read article with its The relevance of the article being written makes it difficult for the author to read efficiently and save the essentials. Therefore, when referring to articles, the author may ignore articles that are highly related to the article he is writing, resulting in the author not being able to obtain them in time. Most needed content.

In order to allow the author to efficiently search for articles that are highly relevant to the content he is writing, to help him expand his knowledge and guide him to quickly understand the depth of what he is reading, so as to help him strengthen what he is writing. The completeness of the content, so how to instantly recommend the articles that the author needs to choose so that the author does not waste time looking for and following shortcuts to deepen the knowledge required for reading, is an urgent problem that every industry needs to solve.

The purpose of the present invention is to provide an editing system for recommending articles. When a user uses the editing system of the present invention to write the first draft of an article, in order to fully express the comments or thoughts he or she wants to express in the first draft of the article, the user can use this system to find and read reference articles related to the first draft of the article. In order to provide a complete discussion of the first draft of his article. When using the system of the present invention to find articles that are highly relevant to the first draft of the article, the first draft of the article can be compared with different types of article groups to find articles that are highly relevant to the first draft of the article, so that The system of the present invention provides these highly relevant articles to users, allowing users to refer to these articles and further adjust or modify the content of the first draft they want to publish. On the one hand, it improves the user's understanding of specific knowledge or issues. The scope of cognition makes its ideas no longer limited to the local area. On the other hand, the recommended articles found through the information analysis of the present invention allow users to refer to these recommended articles and re-examine the theme of the first draft of the article they wrote. Are there any deficiencies in the direction and key content?

Another object of the present invention is to establish multiple topic models and construct multiple two-dimensional maps accordingly, so that users can find a suitable topic model and find recommended articles from the topic model, and the recommended articles Multiple articles will be displayed on the 2D map with numbers. The multiple articles on the cloud server will first separate multiple word combinations and convert the multiple word combinations into plural vectors corresponding to them, so that the plural vectors correspond to the multiple articles. Then the plurality of articles and the corresponding plurality of vectors are divided into multiple groups and a topic model is established based on the multiple groups; the above topic model can also be life investment, financial management, real estate life, or regional general manager, etc. Topic model; each topic model will be further divided into groups (for example, with life investment as the topic model, there will be further details) Separate three sub-categories of industrial thinking, business thinking and investment thinking), and construct a two-dimensional map based on the theme model, and coordinate the plurality of vectors to generate a plurality of coordinates, so that the plurality of coordinates are presented in the two-dimensional on the map.

In addition, the first draft of the article written by the user will first separate the first draft word combination and convert the first draft word combination into a corresponding first draft vector, so that the first draft vector corresponds to the first draft of the article. In the present invention, The system of the present invention can be used to calculate the similarity value between the user's first draft vector and a vector of a group in a topic model and the first draft vector. When the similarity value is higher than a threshold, it is recommended to match the vector Corresponding articles, therefore, when users read the articles recommended by this system, they can adjust the first draft of the article they write to enrich its content by absorbing new knowledge.

In the first aspect of the present invention, the system of the present invention recommends to the user a plurality of articles that are highly relevant to the first draft of the article when the user is writing the first draft of the article. It includes a writing application and a cloud server. The above-mentioned writing application is located in a first electronic device (for example, a handheld electronic device, a desktop computer), and the above-mentioned first electronic device has a first processing device, a first memory, and a wireless transmission module. , wherein the aforementioned first memory, such as a cloud hard drive (Microsoft SkyDrive, Google Drive, Apple iTune) or a general hard drive, is coupled to a first processing device (which may include a CPU, a buffer, a multiplexer and other processing units), for Store the above-mentioned writing application, in which content (including but not limited to text, pictures) is input through an operation interface provided by the above-mentioned writing application to generate a first draft of an article, wherein the aforementioned first draft of the article is also stored in the aforementioned first memory, and the above-mentioned The first draft of the article is further output to the cloud server through the wireless transmission module of the first electronic device.

In a second aspect of the invention, the invention discloses a cloud server. The cloud server further includes a second processing device, a communication interface and a plurality of articles. The communication interface of the cloud server and the wireless transmission module Once the link is complete, the article is transferred from the authoring application The first draft is sent to the cloud server, where the aforementioned cloud server further includes a corpus, conversion module, grouping module, and computing module. The above-mentioned corpus is coupled to the second processing device for storing the first draft of the article, a plurality of articles, a first draft word combination corresponding to the first draft of the article, and a plurality of article word combinations corresponding to the plurality of articles (i.e., each article has a corresponding article word combination), furthermore, the first draft word combination is separated from the first draft of the article, and the plurality of article word combinations is separated from the plurality of articles (i.e., each article word combination is separated from each article article); the above-mentioned conversion module is coupled to the second processing device and the corpus, and has a machine learning model to convert the above-mentioned first draft word combination into a corresponding first draft vector, and the above-mentioned plurality of article word combinations are also converted is a plurality of corresponding vectors, so that the above-mentioned first draft vector corresponds to the first draft of the article, and the plurality of vectors corresponds to the plurality of articles; on the other hand, in order to classify the above-mentioned plurality of articles, in the present invention The cloud server further includes a grouping module, which is coupled to the second processing device, divides the plurality of articles and the plurality of vectors corresponding to them into multiple groups and establishes a topic model based on the multiple groups; the above-mentioned The computing module is coupled to the second processing device, imports the first draft vector into the topic model, selects a group, and uses a first calculation model to calculate the similarity value between one of the vectors in the aforementioned group and the first draft vector. The current number When the similarity value is higher than a threshold, articles corresponding to the vector are recommended. Furthermore, the corresponding relationship between the above-mentioned plural articles and the plural article word combinations is that the above-mentioned plural articles are also separated into their corresponding plural article word combinations; and the corresponding relationship between the first draft of the above-mentioned articles and the first draft word combinations The first draft of the above-mentioned article has also been separated into its corresponding first draft word combinations. Furthermore, the above-mentioned words are further keywords.

In a third aspect of the present invention, the present invention not only discloses an editing system for recommending articles, but also discloses an article recommendation method of the system. The steps include: generating a first draft of an article through an authoring application and transmitting it to the cloud. Servers, wherein the aforementioned cloud servers include plural articles Article; separately separate a first draft word combination and a plurality of article word combinations from the first draft of the above article and the plurality of articles; use a machine learning model to convert the above first draft word combination into a corresponding first draft vector, and the above plurality of articles The word combination is also converted into a plurality of vectors corresponding to it, so that the first draft vector corresponds to the first draft of the article, and the plurality of vectors corresponds to the plurality of articles; the plurality of articles and its corresponding plurality The vectors are divided into multiple groups and a topic model is established based on the multiple groups; the first draft vector is imported into the topic model and a group is selected, and a first calculation model is used to calculate the relationship between one vector of the group and the first draft The similarity value between vectors. When the similarity value is higher than a threshold, the article corresponding to the vector is recommended; if the similarity value is lower than a threshold, the article corresponding to the vector is not recommended. article. In one embodiment, the above machine learning model is further a Doc2Vec model. In some embodiments, the above similarity value is further a cosine similarity value. In one embodiment, the first calculation model further calculates the cosine similarity value between the first draft vector and the above vector. If the angle between the first draft vector and the above vector is smaller, the calculated cosine similarity value is higher. , indicating that the article corresponding to the vector is highly similar to the user's first draft of the article. Therefore, when the similarity value is higher than a threshold, the system of the present invention will recommend the article corresponding to the vector to the user for reference.

In a certain embodiment, the above-mentioned system further includes a separation module coupled to the second processing device, and the first draft of the above-mentioned article and the plurality of articles are respectively separated from the first draft through a second calculation model of the separation module. The word combination and the plurality of article word combinations; in some embodiments, the second algorithm model of the above-mentioned separation module further includes an elastic search (elasticsearch) component to find the first draft word combination from the first draft of the article and the plurality of articles. and the plurality of article word combinations; in another embodiment, the above-mentioned second calculation model further includes an ik word segmentation algorithm, which is used to segment the first draft of the article and the plurality of articles and eliminate unnecessary words (such as , punctuation marks, function words, etc. etc.), to separate the above-mentioned first-draft word combinations from the first draft of the article, and the above-mentioned plural articles are also separated into a plurality of article word combinations (that is, each article is separated into one article word combination).

100:Editing system for recommended articles

102:Writing Applications

104:Cloud server

106:First electronic device

108: First processing device

110: First memory

112:Wireless transmission module

114:Display

116: Second processing device

118: Communication interface

120:Corpus

122:Conversion module

124:Group module

126:Operation module

128:Separation module

130:Data visualization module

A: First draft of the article

A1: First draft word combination

B-J: plural articles

B1-J1: Plural article word combinations

202~210: Steps

300: Editing system for recommended articles

302:Separate module

304:Conversion module

306:Group module

A: First draft of the article

A1: First draft word combination

B-J: plural articles

B1-J1: Plural article word combinations

a: first draft vector

b-j: plural vectors

Mo: topic model

Go1: first group

Go2: The second group

Go3: The third group

Ma: two-dimensional map

1-7:Number

400:Editing system for recommended articles

402:Table

The embodiments of the present invention are illustrated by the examples in the accompanying drawings, but are not intended to limit the present invention. Similar reference numbers in the following drawings refer to similar components.

Figure 1 is a block diagram showing the basic architecture of an editing system for recommending articles and its application architecture.

Figure 2 is a flow chart showing the article recommendation method of the system of the present invention.

FIG. 3(a) is a data flow diagram showing an embodiment of the editing system of the present invention.

Figure 3(b) is an embodiment of the present invention for displaying a two-dimensional map of the present invention.

FIG. 4 shows articles recommended through the present invention using an embodiment.

The present invention will be described with preferred embodiments and viewpoints. Such descriptions explain the system and method of the present invention and are only used to illustrate and not limit the patentable scope of the present invention. Therefore, in addition to the preferred embodiments in the description, the present invention can also be widely implemented in other embodiments.

As shown in FIG. 1 , it is a block diagram showing the basic architecture of an editing system 100 for recommending articles and its application architecture. The system 100 of the present invention is based on the first draft of an article written by a user to find and recommend articles that are highly relevant to the first draft of the article from a plurality of articles on the cloud server. It includes a writing application 102 and a cloud server. 104, wherein the above-mentioned writing application is located in a first electronic device 106 (for example, a handheld electronic device, a desktop computer), and the above-mentioned first electronic device 106 has a first processing device 108 and a first memory. 110. Wireless transmission module 112 and display 114, wherein the first memory 110 is, for example, a cloud hard drive (Microsoft SkyDrive, Google Drive, Apple iTune) or a general hard disk, coupled to the first processing device 108 (which may include a CPU, a buffer, a multiplexer and other processing units), to store the above-mentioned writing application 102, wherein through the above-mentioned writing application An operation interface 1021 provided by 102 inputs content (including but not limited to text and pictures) to generate an article first draft A, in which the aforementioned article first draft A is also stored in the aforementioned first memory 110, and the aforementioned article first draft A is further The information is output to the cloud server 104 through the wireless transmission module 112 of the first electronic device 106 . The display 114 is coupled to the first processing device 108 to display the authoring application 102 .

The system of the present invention further discloses a cloud server 104. The cloud server further includes a second processing device 116, a communication interface 118 and a plurality of articles B-J. The communication interface 118 of the cloud server 104 and the wireless transmission module 112 After the link is completed, the first draft A of the article is transmitted from the writing application 102 to the cloud server. The cloud server 104 further includes a corpus 120, a conversion module 122, a grouping module 124, and a computing module 126. The above-mentioned corpus 120 is coupled to the second processing device 116 for storing the first draft of the article A, the plurality of articles B-J, the first draft word combination A1 and the plurality of article word combinations B1-J1, wherein the first draft word combination A1 is separated from the article First draft A, the plurality of article word combinations B1-J1 are separated from the plurality of articles B-J, that is, the first draft of article A has a corresponding first draft word combination A1, and each article has a corresponding article word combination (for example, article B There is a corresponding article word combination B1). Furthermore, the above-mentioned first draft word combination A1 is separated from the aforementioned article first draft A, and the above-mentioned plurality of article word combinations B1-J1 are separated from the above-mentioned plurality of articles B-J. In some embodiments, the first draft word combination A1 is further composed of multiple words of the first draft of the article, and each of the plurality of article word combinations is further composed of multiple words of each article. (For example, article word combination B1 is further composed of multiple words of article B). Furthermore, the above-mentioned words are further keywords.

In some embodiments, in order to separate the first draft word combination A1 and the plurality of article word combinations B1-J1 from the first draft A of the article and the plurality of articles B-J respectively, the system 100 of the present invention further includes a separation module 128, which is coupled The second processing device 116 separates the first draft word combination and the plurality of article word combinations respectively through a second calculation model of the separation module 128; the above-mentioned second draft The algorithm model further includes an elastic search component to respectively find the first draft word combination A1 and the plurality of article word combinations B1-J1 from the first draft A of the article and the plurality of articles B-J; in one embodiment, the above-mentioned second The algorithm model further includes an ik word segmentation algorithm to separately separate the first draft word combination A1 and the plurality of article word combinations B1-J1 from the first draft A of the article and the plurality of articles B-J.

In one embodiment, the above-mentioned conversion module 122 is coupled to the above-mentioned second processing device 116, which has a machine learning model to convert the first draft word combination A1 into a corresponding first draft vector, and the plurality of article word combinations B1-J1 is also converted into a plurality of corresponding vectors, so that the first draft vector corresponds to the first draft A of the article, and the plurality of vectors corresponds to the plurality of articles B-J (please refer to Figure 3). In one embodiment, the above machine learning model is further a Doc2Vec model.

In some embodiments, in order to classify the plurality of articles B-J, the cloud server of the present invention further includes a grouping module 124, which is coupled to the second processing device 116 to classify the plurality of articles and their corresponding ones. The plurality of vectors are divided into multiple groups and a topic model is established based on the multiple groups; the above-mentioned computing module 126 is coupled to the second processing device 116 to import the first draft vector into the topic model and select a group, And use a first calculation model to calculate the similarity value between one of the vectors in the group and the first draft vector. When the similarity value is higher than a threshold, the article corresponding to the vector is recommended (please refer to the figure below) 3(a)).

In one embodiment, the above system further includes a data visualization module 130, which constructs a two-dimensional map based on the above topic model (please refer to Figure 3(b)), and the aforementioned data visualization module converts the Articles corresponding to this vector are displayed as numbers on the 2D map. On the other hand, the above-mentioned computing module further coordinates the plurality of vectors and the first draft vector to generate a plurality of coordinates respectively, and the above-mentioned plurality of coordinates are further presented on the two-dimensional map through the data visualization module. In one embodiment, the system of the present invention further includes a table generation module to number and tabulate the articles corresponding to the vector.

Referring to Figure 2, Figure 2 is a flow chart of the article recommendation method of the system of the present invention, which corresponds to the above editing system for recommending articles. The method steps 202 to 210 of the present invention will be described in detail below.

The present invention not only discloses an editing system for recommending articles, but also discloses an article recommendation method of the system. As shown in step 202, a first draft of an article is generated through a writing application and transmitted to the cloud server.

As shown in step 204, a first draft word combination and a plurality of article word combinations are separated from the first draft of the article and the plurality of articles respectively.

As shown in step 206, a machine learning model is used to convert the first draft word combination into a corresponding first draft vector, and the plurality of article word combinations are also converted into a plurality of corresponding vectors, so that the first draft The vector corresponds to the first draft of the article, and the plurality of vectors corresponds to the plurality of articles.

As shown in step 208, the plurality of articles and the plurality of corresponding vectors are divided into multiple groups and a topic model is established based on the multiple groups.

As shown in step 210, the first draft vector is imported into the topic model and a group is selected, and a first calculation model is used to calculate the similarity value between one vector of the group and the first draft vector. When the similarity value is high When it reaches a threshold, articles corresponding to the vector are recommended. In some embodiments, the above similarity value is further a cosine similarity value. In one embodiment, the first calculation model further calculates the cosine similarity value between the first draft vector and the above vector. If the angle between the first draft vector and the above vector is smaller, the calculated cosine similarity value is higher. , indicating that the article corresponding to the vector is highly similar to the user's first draft of the article. Therefore, when the similarity value is higher than a threshold, the system of the present invention will recommend the article corresponding to the vector to the user for reference.

Please refer to FIGS. 3(a) and (b). FIGS. 3(a) and (b) are data flow diagrams showing an embodiment of the present invention in the editing system 300 for recommending articles. The first draft A of the above-mentioned article is transmitted from the writing application to the cloud server, and the aforementioned cloud server also contains a plurality of articles B-J, wherein the aforementioned first draft A and the plurality of articles B-J are transmitted through one of the separation modules 302 The two calculation models separately separate the first draft word combination A1 and the plurality of article word combinations B1-J1; and then use the machine learning model of the conversion module 304 to convert the first draft word combination A1 into a corresponding first draft vector a, And the plurality of article word combinations B1-J1 are also converted into corresponding plurality of vectors b-j, so that the above-mentioned first draft vector a corresponds to the first draft A of the article, and the plurality of vectors b-j corresponds to the plurality of articles. B-J; In the system of the present invention, the grouping module 306 is coupled to the second processing device, and divides the plurality of articles B-J and the corresponding plurality of vectors b-j into multiple groups and establishes them based on the multiple groups. A topic model, for example, divides articles B-D and the plurality of vectors b-d corresponding to them into the first group Go1, articles E-G and the plurality of vectors e-g corresponding to them are divided into the second group Go2, and articles The plurality of vectors h-j corresponding to H-J are divided into the third group Go3, so that the above topic model Mo includes the above-mentioned first group Go1, the second group Go2 and the third group Go3, in which the above-mentioned first draft vector a is imported The topic Model Mo selects the above-mentioned first group Go1, and uses a first calculation model to calculate the similarity value between the vector b of the first group Go1 and the first draft vector a, when the similarity value is higher than a threshold , then the article B corresponding to the vector b is recommended; if the similarity value is lower than a threshold, the article B corresponding to the vector b is not recommended.

In an embodiment, the system further includes a data visualization module, which constructs a two-dimensional map based on the topic model, and the data visualization module converts the article corresponding to the vector b to The number is shown on the 2D map. On the other hand, the above-mentioned computing module further coordinates the plurality of vectors b-j and the first draft vector a to generate a plurality of coordinates respectively, and the above-mentioned plurality of coordinates are further presented on the two-dimensional map through the data visualization module.

Please refer to Figure 3(b). Figure 3(b) is an embodiment of the present invention for displaying a two-dimensional map of the present invention. For example, Figure 3(b) is a two-dimensional map Ma formed based on the results generated by the data flow in Figure 3(a). Since the plurality of articles and their corresponding plurality of vectors are further divided into three groups, and a topic model Mo is established based on the three groups, therefore, the above-mentioned topic model Mo is presented from the above-mentioned two-dimensional map by the first The group Go1, the second group Go2 and the third group Go3 are established. In addition, the present invention coordinates the first draft vector a to generate the coordinate a, and in order to recommend the article B corresponding to the vector b, The present invention coordinates the vector b to generate the coordinate b. It can be seen that the article B corresponding to the vector b is the coordinate b displayed on the two-dimensional map with the number "1" by the aforementioned data visualization module, and is different from the first draft. The first draft of the article A corresponding to the vector a is displayed as "this article" at the coordinate a of the two-dimensional map. On the other hand, when the similarity value between other vectors of the first group Go1 and the first draft vector is also higher than a threshold value, the present invention coordinates the other vectors to generate multiple coordinates, then it is also recommended to coordinate the other vectors with the first draft vector. Other articles corresponding to other vectors are displayed in multiple coordinates of the two-dimensional map with numbers through the aforementioned data visualization module, such as the numbers "2~7" shown in Figure 3(b). In one embodiment, when the text corresponding to the vector b When Chapter B is displayed with the number "1" for the aforementioned data visualization module, it means that the vector b of the aforementioned article B has the highest similarity value with the first draft vector.

Please refer to FIG. 4 , which shows articles recommended through the present invention using an embodiment. The editing system 400 for recommending articles of the present invention further includes a table generation module to generate a table 402. For example, if the user imports the first draft vector into the topic model of the "real estate life" category and selects a group of "investment thinking", and uses the above-mentioned first calculation model to calculate multiple vectors of the "investment thinking" group The similarity value between the first draft vector and the first draft vector. When there are five vectors whose similarity values between the first draft vector are all higher than a threshold, then five articles corresponding to the five vectors are recommended. In order to recommend the above-mentioned 5 articles, the system of the present invention includes a table generation module to arrange the above-mentioned 5 articles from high to low according to the similarity value, number and tabulate them. Therefore, the article title in the table is ranked first. 1's "What's the difference between all three-bedroom apartments? Which one is better, a large three-bedroom apartment, a small three-bedroom apartment, or a 2+1 bedroom apartment?" is the article with the highest similarity value between its vector and the first draft vector; the number will also be displayed in Table 402 , the title and author of each article, the topic model (for example, "real estate life" category), the group in the topic model (for example, "investment thinking"), the total click traffic of each recommended article and the past month of traffic.

Several of the methods of the invention are described in their most basic form, but methods may be added to or deleted from any of the invention without departing from the basic scope of the invention, and may Add certain information to or delete certain information from any of the messages described herein. Those skilled in the art will appreciate that several further modifications and variations may be made to the present invention. The specific examples provided herein are not intended to limit the invention but to illustrate the invention.

The above description is the preferred embodiment of the present invention. Those skilled in the art should understand that they are used to illustrate the present invention and not to limit the scope of the claimed patent rights of the present invention. The scope of patent protection shall depend on the appended patent application scope and its equivalent fields. Anyone who is familiar with the skills in this field, Any changes or modifications made without departing from the spirit or scope of this patent shall be equivalent changes or designs completed within the spirit disclosed in this invention, and shall be included in the following patent application scope.

100:Editing system for recommended articles

102:Writing Applications

104:Cloud server

106:First electronic device

108: First processing device

110: First memory

112:Wireless transmission module

114:Display

116: Second processing device

118: Communication interface

120:Corpus

122:Conversion module

124:Group module

126:Operation module

128:Separation module

130:Data visualization module

A: First draft of the article

A1: First draft word combination

B-J: plural articles

B1-J1: Plural article word combinations

Claims (10)

An editing system for recommended articles, which includes: A writing application installed in a first memory of a first electronic device and coupled to a first processing device of the first electronic device to generate a first draft of an article, wherein the first draft of the article is generated through the first electronic device The wireless transmission module output of the device; A cloud server includes a second processing device, a communication interface and a plurality of articles. When the wireless transmission module is connected to the communication interface, the first draft of the article is transmitted from the writing application to the cloud server, wherein This cloud server contains: A corpus coupled to the second processing device for storing the first draft of the article, the plurality of articles, the first draft word combinations and the plurality of article word combinations, wherein the first draft word combination is separated from the first draft of the article, the plurality of article word combinations The system is separated from the plurality of articles; A conversion module, coupled to the second processing device and the corpus, has a machine learning model to convert the first draft word combination into a corresponding first draft vector, and the plurality of article word combinations are also converted into corresponding first draft vectors. A plurality of corresponding vectors, such that the first draft vector corresponds to the first draft of the article, and the plurality of vectors correspond to the plurality of articles; A grouping module, coupled to the second processing device, divides the plurality of articles and the plurality of corresponding vectors into multiple groups and establishes a topic model based on the multiple groups; An operation module coupled to the second processing device, imports the first draft vector into the topic model and selects a group, and uses a first operation model to calculate the similarity value between one vector of the group and the first draft vector , when the similarity value is higher than a threshold, the article corresponding to the vector is recommended. The editing system for recommending articles as described in claim 1, wherein the system further includes a data visualization module that constructs a two-dimensional map based on the topic model. The editing system for recommending articles as described in claim 2, wherein the data visualization module displays the articles corresponding to the vector on the two-dimensional map with numbers. The editing system for recommending articles as described in claim 2, wherein the computing module further coordinates the plurality of vectors and the first draft vector to generate a plurality of coordinates respectively. The editing system for recommending articles as described in request item 4, wherein the plurality of coordinates are presented on the two-dimensional map through the data visualization module. The editing system for recommending articles as described in claim 1, wherein the plurality of articles and the plurality of corresponding vectors are further divided into three groups. The editing system for recommending articles as described in request item 1, wherein the similarity value is further a cosine similarity value. The editing system for recommending articles as described in claim 1, wherein the system further includes a separation module coupled to the second processing device, and the first draft of the article and the plurality of articles are processed through the separation module A second algorithm model separates the first draft word combination and the plurality of article word combinations respectively. The editing system for recommending articles as described in request item 8, wherein the second algorithm model further includes an elastic search component to find the word combination of the first draft and the plurality of articles respectively from the first draft of the article and the plurality of articles. An article word combination. The editing system for recommending articles as described in claim 8, wherein the second algorithm model includes an ik word segmentation algorithm to separate the first draft word combination and the plurality of articles from the first draft of the article and the plurality of articles respectively. Article word combinations.

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