Abstract: Based on large language model(LLM) technology, this paper addresses the demand for intelligent upgrading of the Quan Guo Bao Kan Suo Yin(CNBKSY) platform and proposes an intelligent retrieval system integrating natural language processing(NLP), semantic retrieval, and intelligent Q&A. The system aims to overcome the limitations in traditional systems , such as low retrieval efficiency and insufficient recall and precision. It embodies three key innovations. First, it constructs a unified knowledge representation model by integrating heterogeneous data from multiple sources, overcoming format inconsistencies among literature resources and achieving a significant transition from keyword matching to semantic understanding. Second, the system employs advanced vectorization models such as BERT and BGE, combined with hybrid retrieval strategies including BM25 and Solr-based methods, to achieve precise and efficient document retrieval. Third, the system incorporates an intelligent Q&A module, supporting multi-round natural language search and high-precision question answering. Thetest results demonstrate that the system achieves significant improvements over traditional retrieval methods in terms of efficiency, recall, and precision, providing a viable technical solution for the intelligent development of the CNBKSY platform.

Key words: Natural language processing, Semantic retrieval, Intelligent Q&, A, Multi-source heterogeneous data fusion, Vectorized modeling, Vectorized database, Generative large language models(LLM), Multi-route retrieval