Government affair industry intelligent information retrieval and pushing system and method based on large model

Through the modularly designed intelligent information retrieval system of the government affairs industry, combined with large language models and search enhancement generation technology, the problem that traditional government affairs information retrieval system is difficult to deal with complex semantics and personalized needs is solved, and efficient and accurate information retrieval and personalized push is achieved, which improves the user experience of government affairs information services.

CN120336504APending Publication Date: 2025-07-18SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202510415677.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-03
Publication Date
2025-07-18

AI Technical Summary

Technical Problem

Traditional government information retrieval systems are difficult to deal with complex semantics and personalized needs, and cannot meet the diverse information needs of users.

Method used

Modular design is adopted, combining natural language processing, large language model and search enhancement generation technology, and receiving user queries through intelligent semantic understanding and query analysis modules, using pre-trained large language models for semantic analysis and intent recognition, optimizing query logic with context perception mechanism, and quickly searching related document fragments from the government cloud through semantic-driven efficient search modules, generating enhanced intelligent content, supporting multi-modal output, and achieving accurate push through personalized push and feedback optimization modules.

Benefits of technology

It realizes the full process optimization from semantic understanding to personalized push, provides efficient and accurate information retrieval and push, supports a variety of content forms, ensures the accuracy and credibility of generated content, and dynamically optimizes system performance based on user feedback.

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Abstract

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.
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