Policy interpretation and effect intelligent analysis system and method based on intelligent label

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a policy interpretation and effect intelligent analysis system and method based on an intelligent label, and the system comprises a data collection and preprocessing method, an intelligent label generation method, a policy interpretation method, an effect intelligent analysis method and a user interaction and management method. The method has the beneficial effects that the intelligent label is generated by utilizing a natural language processing technology driven by a large model, automatic classification and key information extraction of the policy document are realized, the policy interpretation and analysis efficiency is improved, in addition, dynamic updating and manual auditing and correction of the label library are supported, the label library can adapt to continuous changes of the policy document, and the policy interpretation and analysis efficiency is improved. And the practicability and reliability of the label library are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically to a policy interpretation and effectiveness intelligent analysis system and method based on smart tags. Background Art

[0002] In today's era of rapid digitalization and informatization, the effective communication and precise implementation of policies are crucial for the stable operation of society and the sustained growth of the economy. Traditional methods of policy interpretation and effectiveness evaluation are no longer able to meet the needs of modern society, mainly due to the following limitations:

[0003] 1) Dependence on manual labor and low efficiency. Traditional policy interpretation mainly relies on manual analysis by experts or policy analysts, which is time-consuming and labor-intensive. The manual interpretation process is cumbersome and difficult to quickly respond to the interpretation needs of a large number of policy texts, especially when policies are frequently updated or released on a large scale.

[0004] 2) Lack of scientific methods and difficulty in data integration. Traditional policy effectiveness evaluation relies heavily on qualitative analysis and simple statistical methods, making it difficult to comprehensively and objectively quantify the effects and impacts of policy implementation. Implementation effectiveness data are usually scattered across various departments, with inconsistent data formats and standards, making them difficult to effectively integrate and analyze.

[0005] To address the above issues, it is necessary to propose a policy interpretation and effectiveness intelligent analysis system based on smart tags. The purpose is to build an intelligent policy interpretation and effectiveness analysis system by utilizing the generalized understanding ability of large models and big data analysis technology to improve the accuracy of policy interpretation and the scientific nature of effectiveness analysis, promote the intelligent upgrade of government services, reduce manual dependence, and improve work efficiency. Summary of the Invention

[0006] The purpose of the present invention is to provide a policy interpretation and effectiveness intelligent analysis system and method based on smart tags to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a policy interpretation and effectiveness intelligent analysis system based on smart labels, comprising a data collection and preprocessing module, a smart label generation module, a policy interpretation module, an effectiveness intelligent analysis module, and a user interaction and management module;

[0008] The data collection and preprocessing module is used to collect policy data and performance data, and perform preliminary purification processing on the collected data;

[0009] The smart label generation module is used to extract key information from the cleaned policy documents through natural language processing technology and build a smart label library;

[0010] The policy interpretation module is used to generate easy-to-understand interpretation content based on smart tags and supports multi-dimensional queries;

[0011] The intelligent performance analysis module is used to match policy data with performance data using an intelligent tag library, apply big data analysis techniques and machine learning algorithms to quantitatively evaluate the matched performance data, and present the results in visual charts and reports;

[0012] The user interaction and management module is used to provide a visual interactive interface, supporting users to view, evaluate and provide feedback on policy interpretation content and effectiveness analysis results, and set permissions based on user identity.

[0013] Preferably, in the data acquisition and preprocessing module:

[0014] For policy data collection, a distributed crawler system is used to regularly capture data from government websites, national policy databases, local government transparency platforms, and authoritative news media. It supports parsing of multiple file formats, including text, PDF, and images. It uses OCR technology to parse images and scanned documents, and builds an abnormal data recognition model based on a dual verification mechanism of regular expressions and semantic analysis. This automatically identifies and filters invalid links and garbled text, achieving preliminary data purification.

[0015] For the collection of effectiveness data, we connect with the government statistical system, official annual report database and authoritative industry reporting platform through standardized data interfaces, and obtain various effectiveness data in the policy implementation process according to the predetermined collection frequency, including economic indicator data, social development data, and industry statistics, and verify the completeness and accuracy of the collected effectiveness data.

[0016] Preferably, the smart tag library constructed by the smart tag generation module covers three categories: basic tags, theme tags, and attribute tags. The construction method is as follows:

[0017] Basic tags: Use regular expressions to accurately extract fixed-format field information such as title, document number, publication date, and validity period from policy documents. Leveraging large-scale model-driven named entity recognition technology, we identify and obtain information about issuing agencies and industry classification entities from the policy document text, generating basic tags that accurately reflect the basic attributes of the policy document.

[0018] Topic tags: Using text summarization technology, we extract key sentences and important information from policy documents. By highly condensing and summarizing these contents, we form concise and intuitive topic tags that reflect the core essence of the policy.

[0019] Attribute labeling: Using text classification technology, policy documents are accurately divided into corresponding categories based on pre-set policy type and scope of application classification standards, and each policy document is assigned a corresponding attribute label;

[0020] In addition, various tags are stored in the tag library according to the hierarchical structure, supporting dynamic updates of the tag library. At the same time, a tag management background is provided to support manual review and correction of tags.

[0021] Preferably, the policy interpretation module can intelligently analyze the user's query needs, combine with the structured tag library, accurately match the most relevant policies from massive policy documents, and support multi-dimensional screening conditions to optimize query results;

[0022] After matching relevant policy documents, the system relies on these policy documents and their associated smart tags, and uses cutting-edge text generation technology to deeply explore the inherent logic of the policy text, comprehensively analyze the policy background, accurately refine the policy objectives, and analyze the main measures for policy implementation in detail. In addition, it combines the results of similar policy implementation in the past to make scientific predictions about the expected effects of this policy. With the help of natural language processing technology, it converts these contents into easy-to-understand and clearly organized text.

[0023] At the same time, it supports multiple output formats, and users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

[0024] Preferably, the effectiveness intelligent analysis module uses a comprehensive intelligent tag library to accurately match policy data with effectiveness data using intelligent tags as a link, and uses the corresponding relationship between tags to establish an effective connection between policy documents and their associated policy implementation effectiveness data;

[0025] After the matching is complete, we use big data analysis technology and machine learning algorithms to conduct an in-depth analysis of the integrated performance data. We use regression analysis algorithms to explore the impact of different factors on performance data during policy implementation. Using cluster analysis algorithms, we divide performance data with similar attributes into different categories based on the inherent characteristics of the data and explore the inherent patterns of data distribution.

[0026] The problems and patterns hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive and easy-to-understand visual charts and detailed reports. Visual charts include but are not limited to bar charts, line charts, and pie charts, and are accompanied by detailed report text to provide a comprehensive and in-depth analysis and interpretation of the data.

[0027] A method for a policy interpretation and effectiveness intelligent analysis system based on smart labels, comprising a data collection and preprocessing method, a smart label generation method, a policy interpretation method, an effectiveness intelligent analysis method, and a user interaction and management method;

[0028] The data collection and preprocessing method is used to collect policy data and performance data, and to perform preliminary purification on the collected data;

[0029] The smart tag generation method is used to extract key information from the cleaned policy documents through natural language processing technology to build a smart tag library;

[0030] The policy interpretation method is used to generate easy-to-understand interpretation content based on smart tags and supports multi-dimensional queries;

[0031] The intelligent effectiveness analysis method is used to match policy data with effectiveness data using an intelligent tag library, apply big data analysis techniques and machine learning algorithms to quantitatively evaluate the matched effectiveness data, and present the results in visual charts and reports;

[0032] The user interaction and management method is used to provide a visual interactive interface, support users to view, evaluate and provide feedback on policy interpretation content and effectiveness analysis results, and set permissions based on user identity.

[0033] Preferably, in the data collection and preprocessing method:

[0034] For policy data collection, a distributed crawler system is used to regularly capture data from government websites, national policy databases, local government transparency platforms, and authoritative news media. It supports parsing of multiple file formats, including text, PDF, and images. It uses OCR technology to parse images and scanned documents, and builds an abnormal data recognition model based on a dual verification mechanism of regular expressions and semantic analysis. This automatically identifies and filters invalid links and garbled text, achieving preliminary data purification.

[0035] For the collection of effectiveness data, we connect with the government statistical system, official annual report database and authoritative industry reporting platform through standardized data interfaces, and obtain various effectiveness data in the policy implementation process according to the predetermined collection frequency, including economic indicator data, social development data, and industry statistics, and verify the completeness and accuracy of the collected effectiveness data.

[0036] Preferably, the smart tag library constructed by the smart tag generation method covers three categories: basic tags, theme tags, and attribute tags, and is constructed as follows:

[0037] Basic tags: Use regular expressions to accurately extract fixed-format field information such as title, document number, publication date, and validity period from policy documents. Leveraging large-scale model-driven named entity recognition technology, we identify and obtain information about issuing agencies and industry classification entities from the policy document text, generating basic tags that accurately reflect the basic attributes of the policy document.

[0038] Topic tags: Using text summarization technology, we extract key sentences and important information from policy documents. By highly condensing and summarizing these contents, we create topic tags that can concisely and intuitively reflect the core essence of the policy.

[0039] Attribute labeling: Using text classification technology, policy documents are accurately divided into corresponding categories based on pre-set policy type and scope of application classification standards, and each policy document is assigned a corresponding attribute label;

[0040] In addition, various tags are stored in the tag library according to the hierarchical structure, supporting dynamic updates of the tag library. At the same time, a tag management background is provided to support manual review and correction of tags.

[0041] Preferably, the policy interpretation method can intelligently analyze the user's query needs, combine with the structured tag library, accurately match the most relevant policies from massive policy documents, and support multi-dimensional screening conditions to optimize query results;

[0042] After matching relevant policy documents, the method relies on these policy documents and their associated smart tags, and uses cutting-edge text generation technology to deeply explore the internal logic of the policy text, comprehensively analyze the policy background, accurately refine the policy objectives, and analyze the main measures for policy implementation in detail. In addition, it combines the results of similar policy implementation in the past to make scientific predictions about the expected effects of this policy. With the help of natural language processing technology, these contents are converted into easy-to-understand and clearly organized texts.

[0043] At the same time, it supports multiple output formats, and users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

[0044] Preferably, the intelligent effectiveness analysis method uses a comprehensive intelligent tag library to accurately match policy data with effectiveness data using intelligent tags as a link, and uses the corresponding relationship between tags to establish an effective connection between policy documents and their associated policy implementation effectiveness data;

[0045] After the matching is complete, we use big data analysis technology and machine learning algorithms to conduct an in-depth analysis of the integrated performance data. We use regression analysis algorithms to explore the impact of different factors on performance data during policy implementation. Using cluster analysis algorithms, we divide performance data with similar attributes into different categories based on the inherent characteristics of the data and explore the inherent patterns of data distribution.

[0046] The problems and patterns hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive and easy-to-understand visual charts and detailed reports. Visual charts include but are not limited to bar charts, line charts, and pie charts, and are accompanied by detailed report text to provide a comprehensive and in-depth analysis and interpretation of the data.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The intelligent policy interpretation and effectiveness analysis system and method based on smart tags proposed in this invention utilizes large-scale model-driven natural language processing technology to generate smart tags, enabling automated classification of policy documents and extraction of key information, thereby improving the efficiency of policy interpretation and analysis. Furthermore, it supports dynamic updating of the tag library and manual review and correction, enabling the tag library to adapt to the constant changes in policy documents, thereby enhancing the practicality and reliability of the tag library. The interpretations generated based on smart tags are comprehensive and easy to understand, and support multiple output formats, meeting the diverse needs of different users and significantly improving the user experience. Using smart tags as a link, it accurately matches policy data with effectiveness data, and conducts in-depth analysis using big data analysis technology and machine learning algorithms. Through regression analysis and cluster analysis, it uncovers hidden problems and patterns in the data, and presents the results in visual charts and detailed reports, providing a scientific and reliable basis for optimizing and adjusting policy interpretations, and helping to improve the scientific nature and effectiveness of policy formulation. Providing a visual interactive interface and permission management functions facilitates user operation while ensuring data security and confidentiality, and has excellent promotional value. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0050] In order to clearly and completely describe the objectives and technical solutions of the present invention and make the advantages more clearly understood, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, not all of them, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] For example 1, please refer to Figure 1 The present invention provides a technical solution: a policy interpretation and effectiveness intelligent analysis system based on smart labels, which mainly includes a data collection and preprocessing module, a smart label generation module, a policy interpretation module, an effectiveness intelligent analysis module and a user interaction and management module. Figure 1As shown in the figure, the data collection and preprocessing module covers two major areas: policy data and performance data. Regarding policy data collection, the module acquires data from multiple sources and, using pre-set filtering rules, accurately removes duplicate, erroneous, and invalid data, achieving a preliminary cleansing of policy data. Regarding performance data collection, the module integrates with government statistical systems, official annual report databases, and authoritative industry reporting platforms to comprehensively collect various performance data from policy implementation. This provides a solid data foundation for subsequent in-depth analysis and ensures data accuracy, completeness, and usability. The intelligent tag generation module extracts key information from cleaned policy documents using large-scale model-driven natural language processing (NLP) technology and constructs an intelligent tag library (basic tags, topic tags, and attribute tags). The policy interpretation module generates easy-to-understand interpretations based on intelligent tags and supports multi-dimensional queries. The intelligent performance analysis module uses the intelligent tag library to match policy data with performance data. It then applies big data analytics and machine learning algorithms to quantitatively evaluate the matched performance data, uncovering hidden issues and patterns in the data. The results are presented in visual charts and reports, providing a scientific basis for optimizing and adjusting policy interpretations. The user interaction and management module provides a visual interactive interface to support users in viewing, evaluating, and providing feedback on policy interpretation content and effectiveness analysis results. It also sets permissions based on user identity to ensure data security and confidentiality. The specific implementation steps of this invention are as follows:

[0052] Step 1: Data acquisition and preprocessing module

[0053] The data collection and preprocessing module of this invention covers two major parts: policy data and performance data. For policy data collection, a distributed crawler system regularly captures data from government websites, national policy databases, local government transparency platforms, and authoritative news media. It supports parsing of various file formats, including text, PDFs, and images, and specifically uses optical character recognition (OCR) technology to parse images and scanned documents. A dual validation mechanism based on regular expressions and semantic analysis is used to construct an abnormal data recognition model, automatically identifying and filtering invalid links, garbled text, and other issues, achieving preliminary data purification.

[0054] For effectiveness data, we connect with the government statistical system, official annual report database and authoritative industry reporting platform through standardized data interfaces, and obtain various effectiveness data in the policy implementation process according to the predetermined collection frequency, including economic indicator data, social development data, industry statistics, etc., and verify the completeness and accuracy of the collected effectiveness data.

[0055] Step 2: Smart Label Module

[0056] The cleaned policy documents are fed into the smart tag generation module. Leveraging large-scale model-driven natural language processing (NLP) technology, this method employs lexical analysis, syntactic analysis, and semantic analysis to comprehensively and deeply analyze policy documents, extract key information from them, and construct a smart tag library. This library encompasses three main categories: basic tags, topic tags, and attribute tags.

[0057] Basic tags: When constructing basic tags, regular expressions are used to accurately extract fixed-format field information from policy documents, such as the title, document number, publication date, and validity period. Simultaneously, large-scale model-driven named entity recognition (NER) technology is used to identify and extract entity information such as issuing agencies and industry classifications from the policy document text. Through these two technical approaches, a series of basic tags are generated that accurately reflect the basic attributes of policy documents.

[0058] Topic tags: In the process of generating topic tags, we use text summarization technology to extract key sentences and important information from policy documents. By highly condensing and summarizing these contents, we form topic tags that can concisely and intuitively reflect the core essence of the policy, allowing users to quickly grasp the core content of the policy document.

[0059] Attribute labels: For attribute labels, text classification technology is used to accurately divide policy documents into corresponding categories based on pre-set classification standards such as policy type and scope of application, and each policy document is assigned a corresponding attribute label.

[0060] After completing tag construction, the present invention stores various tags in a hierarchical structure within a tag library. This library supports dynamic updates, ensuring the system can promptly adapt to changes in policy documents. Furthermore, the present invention provides a tag management backend that supports manual review and revision of tags, further ensuring their accuracy and effectiveness.

[0061] Step 3: Policy interpretation module

[0062] The present invention can intelligently analyze the user's query needs, combine with the structured tag library, accurately match the most relevant policies from massive policy documents, and support multi-dimensional screening conditions (policy subject, release time, release department, etc.) to optimize the query results, ensuring that the search results are highly consistent with user needs.

[0063] Once relevant policy documents are matched, the system leverages these documents and their associated smart tags, using cutting-edge text generation technology, to meticulously craft interpretations. During this generation process, the system deeply explores the inherent logic of the policy text, comprehensively analyzes the policy context, accurately refines the policy objectives, and meticulously analyzes the key implementation measures. Furthermore, it draws on past experience with similar policies to scientifically predict the expected effects of this policy. Leveraging natural language processing technology, this content is transformed into accessible and clearly structured text, making it easily accessible even to non-professionals.

[0064] In order to enhance user experience, the system supports multiple output formats. Users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

[0065] Step 4: Effectiveness Intelligent Analysis Module

[0066] The system leverages a comprehensive smart tag library, using smart tags as a link to accurately match policy data with performance data. Specifically, by leveraging the corresponding relationships between tags, it establishes an effective connection between policy documents and their associated policy implementation performance data, ensuring that each policy has a corresponding set of implementation performance data.

[0067] After completing the matching process, the system applies big data analysis techniques and machine learning algorithms to conduct an in-depth analysis of the integrated performance data. During this process, regression analysis algorithms are used to construct mathematical models between variables to explore the impact of different factors on performance data during policy implementation, thereby clearly grasping the quantitative relationship between each factor and policy effectiveness. Furthermore, cluster analysis algorithms are used to classify performance data with similar attributes into different categories based on the inherent characteristics of the data, thereby exploring the inherent patterns of data distribution.

[0068] After systematic quantitative evaluation and in-depth mining, the problems and patterns hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive, easy-to-understand visual charts and detailed reports. Visual charts include but are not limited to bar charts, which are used to clearly compare the effectiveness of different policies or different time periods; line charts, which show the dynamic changes in effectiveness data during policy implementation; and pie charts, which intuitively reflect the proportion of various effectiveness indicators in the overall situation. In addition, with detailed report text, a comprehensive and in-depth analysis and interpretation of the data is carried out, providing a solid and reliable scientific basis for the optimization and adjustment of policy interpretation, helping policymakers and implementers to accurately grasp the effectiveness of policy implementation, identify problems in a timely manner, and make reasonable adjustments.

[0069] Step 5: User interaction and management module

[0070] The system provides a visual interactive interface that displays policy interpretations and effectiveness analysis results to users, allowing them to review, evaluate, and provide feedback. The system collects user feedback and provides a reference for subsequent system optimization. Furthermore, permissions are set based on user identity, ensuring that only authorized users can access sensitive data, thus ensuring data security and confidentiality.

[0071] Example 2, based on Example 1, proposes a method for a policy interpretation and effectiveness intelligent analysis system based on smart tags, including a data collection and preprocessing method, a smart tag generation method, a policy interpretation method, an effectiveness intelligent analysis method, and a user interaction and management method;

[0072] The data collection and preprocessing method is used to collect policy data and performance data, and to perform preliminary purification on the collected data; for policy data collection, a distributed crawler system is used to implement regular data capture of government official websites, national policy databases, local government affairs disclosure platforms, and authoritative news media, supporting parsing of multiple file formats such as text, PDF, and images, using OCR technology to parse images and scanned copies, and based on a dual verification mechanism of regular expressions and semantic analysis, an abnormal data recognition model is constructed to automatically identify and filter invalid links and garbled text to achieve preliminary purification of data; for performance data collection, a standardized data interface is used to connect with the government statistical system, the official annual report database, and the authoritative industry report platform, and various performance data in the policy implementation process are obtained according to the predetermined collection frequency, including economic indicator data, social development data, and industry statistics, and the collected performance data are verified for integrity and accuracy.

[0073] The smart tag generation method is used to extract key information from cleaned policy documents through natural language processing technology to construct a smart tag library. The smart tag library constructed by the smart tag generation method covers three categories: basic tags, theme tags, and attribute tags. The construction method is as follows:

[0074] Basic tags: Use regular expressions to accurately extract field information with a fixed format such as title, document number, publication time, and validity period from policy documents. At the same time, use large model-driven named entity recognition technology to identify and obtain issuing agency and industry classification entity information from the policy document text, and generate basic tags that accurately reflect the basic attributes of the policy document; Topic tags: With the help of text summarization technology, extract key sentences and important information from policy documents, and through highly condensed and summarized content, form topic tags that can concisely and intuitively reflect the core essence of the policy; Attribute tags: Use text classification technology to accurately divide policy documents into corresponding categories based on pre-set policy type and scope of application classification standards, and assign corresponding attribute tags to each policy document; and store various tags in the tag library according to a hierarchical structure, support dynamic updates of the tag library, and provide a tag management background to support manual review and correction of tags.

[0075] The policy interpretation method is used to generate easy-to-understand interpretation content based on smart tags and supports multi-dimensional queries; the policy interpretation method intelligently analyzes the user's query needs, combines with the structured tag library, accurately matches the most relevant policies from massive policy documents, and supports multi-dimensional screening conditions to optimize query results; after matching relevant policy documents, the method relies on these policy documents and their associated smart tags, with the help of cutting-edge text generation technology, to deeply explore the internal logic of the policy text, comprehensively analyze the policy background, accurately refine the policy goals, and analyze the main measures for policy implementation in detail. In combination with the results of similar policy implementation in the past, a scientific prediction is made on the expected effect of this policy, and with the help of natural language processing technology, these contents are converted into easy-to-understand and clearly organized texts; at the same time, multiple output forms are supported, and users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

[0076] The intelligent effectiveness analysis method is used to match policy data with effectiveness data with the help of an intelligent tag library, use big data analysis technology and machine learning algorithms to quantitatively evaluate the matched effectiveness data, and present the results in visual charts and reports; the intelligent effectiveness analysis method uses a complete intelligent tag library to accurately match policy data with effectiveness data using intelligent tags as a link, and uses the correspondence between tags to establish an effective connection between policy documents and their associated policy implementation effectiveness data; after completing the matching, the integrated effectiveness data is deeply analyzed using big data analysis technology and machine learning algorithms, and the regression analysis algorithm is used to explore the degree of influence of different factors on the effectiveness data during the policy implementation process. With the help of a cluster analysis algorithm, effectiveness data with similar attributes are divided into different categories based on the inherent characteristics of the data, and the inherent laws of data distribution are explored; the problems and laws hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive and easy-to-understand visual charts and detailed reports. The visual charts include but are not limited to bar charts, line charts, and pie charts, and are accompanied by detailed report text to conduct a comprehensive and in-depth analysis and interpretation of the data.

[0077] The user interaction and management method is used to provide a visual interactive interface, support users to view, evaluate and provide feedback on policy interpretation content and effectiveness analysis results, and set permissions based on user identity.

[0078] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A policy interpretation and effectiveness intelligent analysis system based on smart tags, characterized by: It includes data collection and preprocessing module, intelligent label generation module, policy interpretation module, effectiveness intelligent analysis module and user interaction and management module; The data collection and preprocessing module is used to collect policy data and performance data, and perform preliminary purification processing on the collected data; The smart label generation module is used to extract key information from the cleaned policy documents through natural language processing technology and build a smart label library; The policy interpretation module is used to generate easy-to-understand interpretation content based on smart tags and supports multi-dimensional queries; The intelligent performance analysis module is used to match policy data with performance data using an intelligent tag library, apply big data analysis techniques and machine learning algorithms to quantitatively evaluate the matched performance data, and present the results in visual charts and reports; The user interaction and management module is used to provide a visual interactive interface, supporting users to view, evaluate and provide feedback on policy interpretation content and effectiveness analysis results, and set permissions based on user identity.

2. The policy interpretation and effectiveness intelligent analysis system based on smart tags according to claim 1 is characterized by: In the data acquisition and preprocessing module: For policy data collection, a distributed crawler system is used to regularly capture data from government websites, national policy databases, local government transparency platforms, and authoritative news media. It supports parsing of multiple file formats, including text, PDF, and images. It uses OCR technology to parse images and scanned documents, and builds an abnormal data recognition model based on a dual verification mechanism of regular expressions and semantic analysis. This automatically identifies and filters invalid links and garbled text, achieving preliminary data purification. For the collection of effectiveness data, we connect with the government statistical system, official annual report database and authoritative industry reporting platform through standardized data interfaces, and obtain various effectiveness data in the policy implementation process according to the predetermined collection frequency, including economic indicator data, social development data, and industry statistics, and verify the completeness and accuracy of the collected effectiveness data.

3. The policy interpretation and effectiveness intelligent analysis system based on smart tags according to claim 2 is characterized by: The smart tag library built by the smart tag generation module covers three categories: basic tags, theme tags, and attribute tags. The construction method is as follows: Basic tags: Use regular expressions to accurately extract fixed-format field information such as title, document number, publication date, and validity period from policy documents. Leveraging large-scale model-driven named entity recognition technology, we identify and obtain information about issuing agencies and industry classification entities from the policy document text, generating basic tags that accurately reflect the basic attributes of the policy document. Topic tags: Using text summarization technology, we extract key sentences and important information from policy documents. By highly condensing and summarizing these contents, we form concise and intuitive topic tags that reflect the core essence of the policy. Attribute labeling: Using text classification technology, policy documents are accurately divided into corresponding categories based on pre-set policy type and scope of application classification standards, and each policy document is assigned a corresponding attribute label; In addition, various tags are stored in the tag library according to the hierarchical structure, supporting dynamic updates of the tag library. At the same time, a tag management background is provided to support manual review and correction of tags.

4. The policy interpretation and effectiveness intelligent analysis system based on smart tags according to claim 3 is characterized by: The policy interpretation module can intelligently analyze user query needs, combine with the structured tag library, accurately match the most relevant policies from massive policy documents, and support multi-dimensional screening conditions to optimize query results; After matching relevant policy documents, the system relies on these policy documents and their associated smart tags, and uses cutting-edge text generation technology to deeply explore the inherent logic of the policy text, comprehensively analyze the policy background, accurately refine the policy objectives, and analyze the main measures for policy implementation in detail. In addition, it combines the results of similar policy implementation in the past to make scientific predictions about the expected effects of this policy. With the help of natural language processing technology, it converts these contents into easy-to-understand and clearly organized text. At the same time, it supports multiple output formats, and users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

5. The policy interpretation and effectiveness intelligent analysis system based on smart tags according to claim 4 is characterized by: The effectiveness intelligent analysis module uses a comprehensive intelligent tag library to accurately match policy data with effectiveness data using intelligent tags as a link, and uses the corresponding relationship between tags to establish an effective connection between policy documents and their associated policy implementation effectiveness data; After the matching is complete, we use big data analysis technology and machine learning algorithms to conduct an in-depth analysis of the integrated performance data. We use regression analysis algorithms to explore the impact of different factors on performance data during policy implementation. Using cluster analysis algorithms, we divide performance data with similar attributes into different categories based on the inherent characteristics of the data and explore the inherent patterns of data distribution. The problems and patterns hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive and easy-to-understand visual charts and detailed reports. Visual charts include but are not limited to bar charts, line charts, and pie charts, and are accompanied by detailed report text to provide a comprehensive and in-depth analysis and interpretation of the data.

6. A method for the policy interpretation and effectiveness intelligent analysis system based on smart tags according to claim 5, characterized in that: Including data collection and preprocessing methods, intelligent label generation methods, policy interpretation methods, intelligent effectiveness analysis methods, and user interaction and management methods; The data collection and preprocessing method is used to collect policy data and performance data, and to perform preliminary purification on the collected data; The smart tag generation method is used to extract key information from the cleaned policy documents through natural language processing technology to build a smart tag library; The policy interpretation method is used to generate easy-to-understand interpretation content based on smart tags and supports multi-dimensional queries; The intelligent effectiveness analysis method is used to match policy data with effectiveness data using an intelligent tag library, apply big data analysis techniques and machine learning algorithms to quantitatively evaluate the matched effectiveness data, and present the results in visual charts and reports; The user interaction and management method is used to provide a visual interactive interface, support users to view, evaluate and provide feedback on policy interpretation content and effectiveness analysis results, and set permissions based on user identity.

7. A method according to claim 6, characterized in that: In the data acquisition and preprocessing method: For policy data collection, a distributed crawler system is used to regularly capture data from government websites, national policy databases, local government transparency platforms, and authoritative news media. It supports parsing of multiple file formats, including text, PDF, and images. It uses OCR technology to parse images and scanned documents, and builds an abnormal data recognition model based on a dual verification mechanism of regular expressions and semantic analysis. This automatically identifies and filters invalid links and garbled text, achieving preliminary data purification. For the collection of effectiveness data, we connect with the government statistical system, official annual report database and authoritative industry reporting platform through standardized data interfaces, and obtain various effectiveness data in the policy implementation process according to the predetermined collection frequency, including economic indicator data, social development data, and industry statistics, and verify the completeness and accuracy of the collected effectiveness data.

8. A method according to claim 7, characterized in that: The smart tag library constructed by the smart tag generation method includes three categories: basic tags, theme tags, and attribute tags. The construction method is as follows: Basic tags: Use regular expressions to accurately extract fixed-format field information such as title, document number, publication date, and validity period from policy documents. Leveraging large-scale model-driven named entity recognition technology, we identify and obtain information about issuing agencies and industry classification entities from the policy document text, generating basic tags that accurately reflect the basic attributes of the policy document. Topic tags: Using text summarization technology, we extract key sentences and important information from policy documents. By highly condensing and summarizing these contents, we create topic tags that can concisely and intuitively reflect the core essence of the policy. Attribute labeling: Using text classification technology, policy documents are accurately divided into corresponding categories based on pre-set policy type and scope of application classification standards, and each policy document is assigned a corresponding attribute label; In addition, various tags are stored in the tag library according to the hierarchical structure, supporting dynamic updates of the tag library. At the same time, a tag management background is provided to support manual review and correction of tags.

9. A method according to claim 8, characterized in that: The policy interpretation method intelligently analyzes the user's query needs, combines with a structured tag library, accurately matches the most relevant policies from massive policy documents, and supports multi-dimensional screening conditions to optimize query results; After matching relevant policy documents, the method relies on these policy documents and their associated smart tags, and uses cutting-edge text generation technology to deeply explore the internal logic of the policy text, comprehensively analyze the policy background, accurately refine the policy objectives, and analyze the main measures for policy implementation in detail. In addition, it combines the results of similar policy implementation in the past to make scientific predictions about the expected effects of this policy. With the help of natural language processing technology, these contents are converted into easy-to-understand and clearly organized texts. At the same time, it supports multiple output formats, and users can choose different presentation methods such as concise summary, detailed interpretation or policy comparison analysis according to actual needs.

10. A method according to claim 9, characterized in that: The intelligent effectiveness analysis method uses a comprehensive intelligent tag library to accurately match policy data with effectiveness data using intelligent tags as a link, and uses the corresponding relationship between tags to establish an effective connection between policy documents and their associated policy implementation effectiveness data; After the matching is complete, we use big data analysis technology and machine learning algorithms to conduct an in-depth analysis of the integrated performance data. We use regression analysis algorithms to explore the impact of different factors on performance data during policy implementation. Using cluster analysis algorithms, we divide performance data with similar attributes into different categories based on the inherent characteristics of the data and explore the inherent patterns of data distribution. The problems and patterns hidden behind the data are sorted out and summarized, and presented to users in the form of intuitive and easy-to-understand visual charts and detailed reports. Visual charts include but are not limited to bar charts, line charts, and pie charts, and are accompanied by detailed report text to provide a comprehensive and in-depth analysis and interpretation of the data.

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