Government affair information management system based on data matching

Abstract

The invention discloses a government affair information management system based on data matching, and relates to the technical field of data management. According to the method, cross-modal keyword association is realized through multi-modal fusion and accurate extraction, and the multi-modal matching accuracy is improved; the key information recall rate is improved through dynamic weight optimization; the BERT semantic vector and the source department weight are combined to realize intelligent classification; and a time similarity factor is introduced, so that the matching priority of recent policy documents is improved, and the error association rate is reduced. Layered encryption storage and dynamic authority control are adopted, the decryption throughput and the unauthorized access interception rate are improved, and user retrieval efficiency and cross-device compatibility are improved through intelligent association retrieval and multi-modal result presentation.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a government information management system based on data matching. Background Art

[0002] At present, government information management mainly relies on traditional technologies, which have significant bottlenecks. In terms of data processing, the system mostly uses single-modal analysis (such as plain text), lacks the ability to integrate and process multi-source data such as images and videos, resulting in cross-modal information fragmentation. For example, the text of flood prevention notices and material dispatch images cannot be automatically associated and require manual matching. Keyword extraction relies on traditional algorithms such as TF-IDF, which makes it difficult to identify compound terms, ignores the weight of file names, and does not combine the authority of data sources. The classification results are easily interfered by high-frequency common words. These problems lead to inefficient government information management, difficulties in cross-departmental collaboration, and difficulty in meeting the needs of digital government construction. Summary of the invention

[0003] The purpose of the present invention is to provide a government information management system based on data matching to improve the above technical problems.

[0004] In order to achieve the above-mentioned object of the invention, the embodiment of the present invention provides the following technical solutions:

[0005] A government information management system based on data matching includes:

[0006] The government information collection and processing module is used to obtain the current batch of government information and pre-process it to obtain the pre-processed government information;

[0007] A keyword group extraction and classification module is used to extract and classify keyword groups in the pre-processed government information to obtain corresponding classification results;

[0008] A keyword group matching module is used to match the current batch of government affairs information with the historical batch of government affairs information according to the keyword group and the classification result to obtain the corresponding matching result;

[0009] The identity verification module is used to verify the identity of the user and obtain the user's keyword phrases and user classification results;

[0010] A user keyword group search module is used to search and decrypt user keyword groups based on user classification results, obtain search results, and complete the management of government information;

[0011] The storage module is used to encrypt / decrypt and store government information and keyword groups, classification results and matching results.

[0012] Furthermore, the preprocessed government information includes encoded text data, RGB-image data and RGB-video data.

[0013] Furthermore, the processing process of the keyword group extraction and classification module includes:

[0014] Using the TF-IDF algorithm and the BERT-TextRank hybrid model, keywords are extracted from the encoded text data, the RGB-image data, and the RGB-video data to obtain the corresponding first keyword group, the second keyword group, and the third keyword group;

[0015] Calculate the relevance of the first keyword group with the second keyword group and the third keyword group respectively;

[0016] Merge the first keyword group, the second keyword group, or / and the third keyword group whose relevance meets the fusion condition, and retain the remaining first keyword group, second keyword group, and third keyword group to obtain a keyword group;

[0017] The Bert-LightGBM model is used to classify the keyword groups to obtain corresponding classification results; the classification results include policy, administration, social services and others.

[0018] Furthermore, the keyword extraction process includes:

[0019] The coded text data and its file name are concatenated, segmented and removed to obtain the processed coded text; the word weight is set, and the BERT-TextRank hybrid model is used to perform semantic analysis on the processed coded text, and the high-frequency words with the top 5 frequencies are selected to determine the first keyword group;

[0020] Extracting first text data from the RGB-image data using the OCR-ResNet model; splicing the first text data and its corresponding file name; processing the spliced ​​first text data using the same method as the first keyword group extracted from the encoded text data to determine a second keyword group;

[0021] The RGB-video data is converted into second text data by using a voice converter; N video frames are randomly intercepted, and the corresponding third text data are extracted by using an OCR-ResNet model; the second text data, the third text data and their file names are spliced ​​to obtain fourth text data; the spliced ​​fourth text data is processed by the same method as the first keyword group of the encoded text data to determine the third keyword group.

[0022] Further, the calculating of the relevance of the first keyword group with the second keyword group and the third keyword group respectively includes:

[0023] Based on the vectors corresponding to the first keyword group and the second keyword group, the corresponding cosine similarity is calculated;

[0024] Based on the keywords corresponding to the first keyword group and the second keyword group, calculating the corresponding semantic vector similarity;

[0025] Set the relevance weight coefficient and rule weight;

[0026] Based on cosine similarity, semantic vector similarity, relevance weight coefficient, and rule weight, the corresponding relevance is calculated;

[0027] The calculation process of the correlation between the first keyword group and the third keyword group is the same as the calculation process of the correlation between the first keyword group and the second keyword group.

[0028] Furthermore, the keyword group is classified using the Bert-LightGBM model to obtain corresponding classification results, including:

[0029] Get the corresponding source department and set the corresponding source weight;

[0030] The semantic vector of the keyword group is extracted using the Bert model and combined with the source weight to obtain the feature vector;

[0031] The feature vector is input into the LightGBM model and the classification result is output.

[0032] Furthermore, the processing process of the keyword group matching module includes:

[0033] Based on the classification results, the historical batches of government information and their corresponding keyword groups in the corresponding database are called; the correlation between the keyword groups of the current batch and the keyword groups of the historical batches is calculated; the government information of the historical batch with the highest correlation is selected, and a link is constructed between it and the current batch of government information to obtain the corresponding matching results.

[0034] Furthermore, the calculation of the correlation between the keyword groups of the current batch and the keyword groups of the historical batches includes:

[0035] Calculate the initial semantic similarity between each keyword in the keyword group of the current batch and each keyword in the keyword group of the historical batch respectively;

[0036] Calculate the average of each initial semantic similarity and use it as the semantic similarity;

[0037] Based on the intersection and union of the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding word frequency similarity is calculated;

[0038] Based on the storage time corresponding to the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding time similarity is calculated;

[0039] Based on semantic similarity, word frequency similarity and time similarity, the corresponding correlation is calculated.

[0040] Furthermore, the identity verification module includes:

[0041] Obtain the user's identity information or work badge information and perform authentication; if the authentication is successful, input the user's keyword group through the client, classify the user's keyword group through the keyword group extraction and classification module, and obtain the user classification result; otherwise, the authentication fails.

[0042] Furthermore, the storage module includes:

[0043] An encryption / decryption unit, used to encrypt / decrypt government information and its keyword groups, classification results and matching results;

[0044] A keyword storage unit, used for storing keyword groups;

[0045] The political and government affairs information storage unit is used for all batches of political and government affairs information and classification results and matching results;

[0046] Administrative government information storage unit, used for all batches of administrative government information and classification results and matching results;

[0047] The social service government affairs information storage unit is used for all batches of social service government affairs information and classification results and matching results;

[0048] Other types of government affairs information storage unit, used for all batches of other types of government affairs information and classification results and matching results.

[0049] The beneficial effects of the present invention are:

[0050] The present invention realizes cross-modal keyword association and improves the accuracy of multimodal matching through multimodal fusion and precise extraction; dynamic weight optimization improves the recall rate of key information; combines BERT semantic vectors with source department weights to achieve intelligent classification; introduces time similarity factors to improve the matching priority of recent policy documents and reduce the false association rate; adopts layered encrypted storage and dynamic permission management to improve decryption throughput and unauthorized access interception rate; intelligent association retrieval and multimodal result presentation improve user retrieval efficiency and cross-device compatibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A system structure diagram in an embodiment of the present invention;

[0053] Figure 2 A structural diagram of a storage module in an embodiment of the present invention;

[0054] Figure 3 Flow chart of the method in the embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0056] See also Figure 1 , this embodiment provides a government information management system based on data matching, which includes:

[0057] The government information collection and processing module is used to obtain the current batch of government information and perform preprocessing, encode unstructured text data such as PDF, TXT, and Word, and convert the format of image data and video data to obtain preprocessed government information, that is, to obtain encoded text data, RGB-image data, and RGB-video data;

[0058] A keyword group extraction and classification module is used to extract and classify keyword groups in the pre-processed government information to obtain corresponding classification results;

[0059] The processing process of the keyword group extraction classification module includes:

[0060] S21, using the TF-IDF algorithm and the BERT-TextRank hybrid model to perform keyword extraction on the encoded text data, the RGB-image data, and the RGB-video data to obtain the corresponding first keyword group, the second keyword group, and the third keyword group;

[0061] The S21 includes:

[0062] Concatenate, segment, and remove the encoded text data and its file name, removing common but meaningless words such as "de", "shi", "zai", etc. to reduce the text complexity and obtain the processed encoded text; set the word weights, and use the BERT-TextRank hybrid model to perform semantic analysis on the processed encoded text to determine the first keyword group;

[0063] Use the BERT-TextRank hybrid model to perform semantic analysis on the processed encoded text, select the top 5 high-frequency words in terms of frequency, and determine the first keyword group, including:

[0064] Use the TextRank algorithm to process the processed encoded text, extract the corresponding key sentences; input the key sentences and the processed encoded text into the BERT model, and output the fused text representation; use the TF-IDF algorithm to count the high-frequency words in the fused text representation, multiply the frequency of the keywords extracted from the name by the word weight 1.5, and multiply the frequency of the keywords extracted from the text data by the word weight 1.0, select the top 5 high-frequency words in terms of frequency, and determine the first keyword group.

[0065] Use the OCR-ResNet model to extract the first text data of the RGB-image data; concatenate the first text data and its corresponding file name; use the same method as extracting the first keyword group of the encoded text data to process the concatenated first text data to determine the second keyword group.

[0066] Use the speech converter to convert the RGB-video data into the second text data; randomly intercept N video frames, and use the OCR-ResNet model to extract the corresponding third text data; concatenate the second text data, the third text data and their file names to obtain the fourth text data; use the same method as extracting the first keyword group of the encoded text data to process the concatenated fourth text data to determine the third keyword group.

[0067] Precisely extract high-frequency keywords from the three aspects of text, image, and video, set the corresponding weights, which can prevent important keywords from being ignored, improve the accuracy and comprehensiveness of subsequent retrieval, and be beneficial to the accuracy of subsequent classification.

[0068] S22. Calculate the relevance between the first keyword group and the second keyword group and the third keyword group respectively;

[0069] The said S22 includes:

[0070] Based on the vectors corresponding to the first keyword group and the second keyword group, calculate the corresponding cosine similarity;

[0071] Based on the keywords corresponding to the first keyword group and the second keyword group, calculate the corresponding semantic vector similarity;

[0072] Set the relevance weight coefficient and rule weight;

[0073] Based on cosine similarity, semantic vector similarity, relevance weight coefficient, and rule weight, the corresponding relevance is calculated;

[0074] The calculation process of the correlation between the first keyword group and the third keyword group is the same as the calculation process of the correlation between the first keyword group and the second keyword group.

[0075] Furthermore, the relevance of this embodiment The formula is:

[0076] ;

[0077] ;

[0078] ;

[0079] in, , represents the correlation weight coefficient, , Represent the cosine similarity function and sine function respectively, , Respectively represent the first keyword group, Keyword group, , Respectively represent the first keyword group , No. Keyword Group The corresponding vector, , Respectively represent the first keyword group The Keywords, Keyword Group The Keywords, represents the maximum value function, represents the first keyword group after normalization, represents the rule weight, Indicates the first keyword group , No. Keyword Group The corresponding cosine similarity is, Indicates the first keyword group , No. Keyword Group The corresponding semantic vector similarity.

[0080] When keywords involve emergency measures, weight rules The value is 0.2; in other cases, the weight rule The value is 0.

[0081] S23. Merge the first keyword group, the second keyword group or / and the third keyword group whose relevance meets the fusion condition, retain the remaining first keyword group, second keyword group and third keyword group to obtain the keyword group; the fusion condition is: if the relevance between the first keyword group and the second keyword group is greater than the first fusion threshold and the relevance between the first keyword group and the third keyword group is greater than the second fusion threshold, then merge the corresponding first keyword group, second keyword group and third keyword group to obtain an updated keyword group; if the relevance is greater than the first fusion threshold or the relevance between the first keyword group and the third keyword group is greater than the second fusion threshold, then merge the corresponding first keyword group and the second keyword group or merge the corresponding first keyword group and the third keyword group to obtain an updated keyword group; in other cases, retain the corresponding first keyword group, second keyword group and third keyword group.

[0082] The keyword group includes the updated keyword group and the first keyword group, the second keyword group, and the third keyword group whose relevance does not exceed the first fusion threshold or the second fusion threshold.

[0083] S24. Use the Bert-LightGBM model to classify the keyword groups and obtain corresponding classification results; wherein the classification results include policy, administration, social services and others.

[0084] The S24 includes:

[0085] Get the corresponding source department and set the corresponding source weight. For example, the source weight of documents issued by the State Council is 1.5, the source weight of documents issued by bureaus and departments is 1.0, and the source weight of documents issued by districts and counties is 0.8. Use the Bert model to extract the semantic vector of the keyword group and combine it with the source weight to obtain a feature vector in the format of [semantic vector, source weight, number of keywords]. Input the feature vector into the LightGBM model and output the classification result.

[0086] The training process of the LightGBM model is:

[0087] Set the initial model parameters of the LightGBM model, the number of leaf nodes is 31, the learning rate is 0.05, and the number of base learners (trees) is 200;

[0088] Obtain training feature vectors and test feature vectors; input the training feature vectors into the LightGBM model, train the LightGBM model using the incremental learning mechanism, adjust the model parameters of the LightGBM model in real time, and obtain the LightGBM model after initial training;

[0089] The test feature vector is input into the LightGBM model after initial training to obtain the corresponding test results; based on the test results, a corresponding confusion matrix is ​​formulated according to the number of correct classifications and incorrect classifications; in this embodiment, the confusion matrix is ​​shown in Table 1.

[0090] Table 1

[0091]

[0092] Based on the confusion matrix, analyze the LightGBM model after initial training, obtain the corresponding analysis results, and calculate the corresponding evaluation indicators, such as F2 score, recall rate, etc.;

[0093] Based on the analysis results, the model parameters of the initially trained LightGBM model are adjusted to obtain the trained LightGBM model.

[0094] A keyword group matching module is used to match the current batch of government affairs information with the historical batch of government affairs information according to the keyword group and the classification result to obtain the corresponding matching result;

[0095] The processing process of the keyword group matching module includes:

[0096] Based on the classification results, the historical batches of government information and their corresponding keyword groups in the corresponding database are called; the correlation between the keyword groups of the current batch and the keyword groups of the historical batches is calculated; the government information of the historical batch with the highest correlation is selected, and a link is constructed between it and the current batch of government information to obtain the corresponding matching results.

[0097] The calculation of the correlation between the keyword groups of the current batch and the keyword groups of the historical batches includes:

[0098] Calculate the initial semantic similarity between each keyword in the keyword group of the current batch and each keyword in the keyword group of the historical batch respectively;

[0099] Calculate the average of each initial semantic similarity and use it as the semantic similarity;

[0100] Based on the intersection and union of the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding word frequency similarity is calculated;

[0101] Based on the storage time corresponding to the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding time similarity is calculated;

[0102] Based on semantic similarity, word frequency similarity and time similarity, the corresponding correlation is calculated.

[0103] Therefore, the relevance of this embodiment The formula is:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] in, , , represents the correlation weight coefficient, , , Represent semantic similarity, word frequency similarity and time similarity respectively. , Respectively represent the current batch Keyword Groups , the historical batch Keyword Groups The corresponding vector, , Separate vector and vector The intersection size and union size of represents the cosine similarity, , Represents keyword groups and keyword groups The first keyword in represents the summation function, , Represents keyword groups and keyword groups The total number of keywords in , Represents keyword groups No. Keywords, keyword groups The Keywords, Indicates keyword group and keyword groups The corresponding time difference, Indicates the maximum time difference.

[0109] By calculating the correlation between the keyword groups of the current batch and the keyword groups of the historical batches, the most relevant historical information to the current government information can be quickly found to improve data matching efficiency. By introducing time similarity, the matching priority of recent policy documents is increased, and the misassociation rate between old policies and new documents is reduced. By building links between the current batch and historical batches of government information, the relevance and traceability of data are enhanced.

[0110] The identity verification module is used to verify the identity of the user and obtain the user's keyword phrases and user classification results;

[0111] The identity verification module comprises:

[0112] Obtain the user's identity information or work badge information and perform authentication; if the authentication is successful, input the user's keyword group through the client, classify the user's keyword group through the keyword group extraction and classification module, and obtain the user classification result; otherwise, the authentication fails.

[0113] A user keyword group search module is used to search and decrypt user keyword groups based on user classification results, obtain search results, and complete the management of government information;

[0114] The search results include the government information with the highest correlation to the user's keyword group and the government information that is connected to the government information. The connection refers to other government information with the highest correlation to the keywords of the government information and the closest time, which can provide additional information to the user. The user can obtain similar government information in the above time period without additional search, reducing the search workload and working time, and obtaining relevant documents in the shortest time.

[0115] Based on the user classification result, the corresponding storage unit is determined; in the storage unit, the government information corresponding to the keyword group with the highest correlation with the user's keyword group is searched; based on the connection, other government information related to the government information is determined; after the searched government information is decrypted, it is packaged into a retrieval report and output to the client.

[0116] The storage module is used to encrypt / decrypt and store government information and keyword groups, classification results and matching results.

[0117] like Figure 2 As shown, the keyword group is stored in the corresponding keyword storage unit; based on the classification result, the corresponding government information and the matching result are encrypted and input into the corresponding storage unit, that is, respectively stored in the political government information storage unit, the administrative government information storage unit, the social service government information storage unit and other government information storage units, so that the storage module includes:

[0118] An encryption / decryption unit, used to encrypt / decrypt government information and its keyword groups, classification results and matching results;

[0119] A keyword storage unit, used for storing keyword groups;

[0120] The political and government affairs information storage unit is used for all batches of political and government affairs information and classification results and matching results;

[0121] Administrative government information storage unit, used for all batches of administrative government information and classification results and matching results;

[0122] The social service government affairs information storage unit is used for all batches of social service government affairs information and classification results and matching results;

[0123] Other types of government affairs information storage unit, used for all batches of other types of government affairs information and classification results and matching results.

[0124] Adopt hierarchical encryption storage to encrypt different types of government information (text, images, videos) to improve data security. Store government information and its keyword groups, classification results, and matching results in different storage units for easy management and retrieval. By optimizing the encryption algorithm and storage structure, the decryption throughput is improved and the data access delay is reduced.

[0125] like Figure 3 As shown, the government information management method corresponding to the government information management system includes:

[0126] S1. Obtaining the government affairs information of the current batch and preprocessing it to obtain the preprocessed government affairs information; wherein the preprocessed government affairs information includes encoded text data, RGB-image data and RGB-video data;

[0127] S2, extracting, associating and classifying keywords of the preprocessed government information to obtain corresponding keyword groups and classification results;

[0128] S3. Based on the keyword group and the classification result, the government information and the government information of the historical batches are matched to obtain the corresponding matching results;

[0129] S4. Encrypt the government information and keyword groups, classification results and matching results and store them in the database (storage module); store the keyword groups in the corresponding keyword storage units; based on the classification results, encrypt the corresponding government information and matching results and input them into the corresponding storage units, that is, store them in the political government information storage unit, administrative government information storage unit, social service government information storage unit and other government information storage units respectively.

[0130] S5. Authentication of the user; obtaining the user keyword group and user classification results;

[0131] S6. Based on the user classification results, the user keyword phrases are searched and decrypted to obtain search results, thereby completing the management of government information.

[0132] In summary, the present invention realizes cross-modal keyword association and improves the accuracy of multimodal matching through multimodal fusion and precise extraction; dynamic weight optimization improves the recall rate of key information; combines BERT semantic vectors with source department weights to achieve intelligent classification; introduces time similarity factors to improve the matching priority of recent policy documents and reduce the misassociation rate; adopts layered encrypted storage and dynamic permission management to improve decryption throughput and unauthorized access interception rate, and intelligent association retrieval and multimodal result presentation improve user retrieval efficiency and cross-device compatibility. The above is only a specific implementation method of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A government information management system based on data matching, characterized in that: include: The government information collection and processing module is used to obtain the current batch of government information and pre-process it to obtain the pre-processed government information; A keyword group extraction and classification module is used to extract and classify keyword groups in the pre-processed government information to obtain corresponding classification results; A keyword group matching module is used to match the current batch of government affairs information with the historical batch of government affairs information according to the keyword group and the classification result to obtain the corresponding matching result; The identity verification module is used to verify the identity of the user and obtain the user's keyword phrases and user classification results; A user keyword group search module is used to search and decrypt user keyword groups based on user classification results, obtain search results, and complete the management of government information; The storage module is used to encrypt / decrypt and store government information and keyword groups, classification results and matching results.

2. The government affairs information management system based on data matching according to claim 1 is characterized in that: The preprocessed government information includes encoded text data, RGB-image data and RGB-video data.

3. The government affairs information management system based on data matching according to claim 2 is characterized in that: The processing process of the keyword group extraction classification module includes: Using the TF-IDF algorithm and the BERT-TextRank hybrid model, keywords are extracted from the encoded text data, the RGB-image data, and the RGB-video data to obtain the corresponding first keyword group, the second keyword group, and the third keyword group; Calculate the relevance of the first keyword group with the second keyword group and the third keyword group respectively; Merge the first keyword group, the second keyword group, or / and the third keyword group whose relevance meets the fusion condition, and retain the remaining first keyword group, second keyword group, and third keyword group to obtain a keyword group; The Bert-LightGBM model is used to classify the keyword groups to obtain corresponding classification results; the classification results include policy, administration, social services and others.

4. The government affairs information management system based on data matching according to claim 3 is characterized in that: The keyword extraction process includes: The coded text data and its file name are concatenated, segmented and removed to obtain the processed coded text; the word weight is set, and the BERT-TextRank hybrid model is used to perform semantic analysis on the processed coded text, and the high-frequency words with the top 5 frequencies are selected to determine the first keyword group; Extracting first text data from the RGB-image data using the OCR-ResNet model; splicing the first text data and its corresponding file name; processing the spliced ​​first text data using the same method as the first keyword group extracted from the encoded text data to determine a second keyword group; The RGB-video data is converted into second text data by using a voice converter; N video frames are randomly intercepted, and the corresponding third text data are extracted by using an OCR-ResNet model; the second text data, the third text data and their file names are spliced ​​to obtain fourth text data; the spliced ​​fourth text data is processed by the same method as the first keyword group of the encoded text data to determine the third keyword group.

5. The government affairs information management system based on data matching according to claim 3 is characterized in that: The calculating the relevance of the first keyword group with the second keyword group and the third keyword group respectively includes: Based on the vectors corresponding to the first keyword group and the second keyword group, the corresponding cosine similarity is calculated; Based on the keywords corresponding to the first keyword group and the second keyword group, calculating the corresponding semantic vector similarity; Set the relevance weight coefficient and rule weight; Based on cosine similarity, semantic vector similarity, relevance weight coefficient, and rule weight, the corresponding relevance is calculated; The calculation process of the correlation between the first keyword group and the third keyword group is the same as the calculation process of the correlation between the first keyword group and the second keyword group.

6. The government affairs information management system based on data matching according to claim 3 is characterized in that: The Bert-LightGBM model is used to classify the keyword groups to obtain corresponding classification results, including: Get the corresponding source department and set the corresponding source weight; The semantic vector of the keyword group is extracted using the Bert model and combined with the source weight to obtain the feature vector; The feature vector is input into the LightGBM model and the classification result is output.

7. The government affairs information management system based on data matching according to claim 1 is characterized in that: The processing process of the keyword group matching module includes: Based on the classification results, the historical batches of government information and their corresponding keyword groups in the corresponding database are called; the correlation between the keyword groups of the current batch and the keyword groups of the historical batches is calculated; the government information of the historical batch with the highest correlation is selected, and a link is constructed between it and the current batch of government information to obtain the corresponding matching results.

8. The government affairs information management system based on data matching according to claim 7 is characterized in that: The calculation of the correlation between the keyword groups of the current batch and the keyword groups of the historical batches includes: Calculate the initial semantic similarity between each keyword in the keyword group of the current batch and each keyword in the keyword group of the historical batch respectively; Calculate the average of each initial semantic similarity and use it as the semantic similarity; Based on the intersection and union of the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding word frequency similarity is calculated; Based on the storage time corresponding to the keyword groups of the current batch and the keyword groups of the historical batches, the corresponding time similarity is calculated; Based on semantic similarity, word frequency similarity and time similarity, the corresponding correlation is calculated.

9. The government affairs information management system based on data matching according to claim 1 is characterized in that: The identity verification module comprises: Obtain the user's identity information or work badge information and perform authentication; if the authentication is successful, input the user's keyword group through the client, classify the user's keyword group through the keyword group extraction and classification module, and obtain the user classification result; otherwise, the authentication fails.

10. The government affairs information management system based on data matching according to claim 1, characterized in that: The storage module comprises: An encryption / decryption unit, used to encrypt / decrypt government information and its keyword groups, classification results and matching results; A keyword storage unit, used for storing keyword groups; The political and government affairs information storage unit is used for all batches of political and government affairs information and classification results and matching results; Administrative government information storage unit, used for all batches of administrative government information and classification results and matching results; The social service government affairs information storage unit is used for all batches of social service government affairs information and classification results and matching results; Other types of government affairs information storage unit, used for all batches of other types of government affairs information and classification results and matching results.

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