Research on intelligent retrieval system of wild animal protection law by integrating knowledge map and natural language processing

This paper focuses on the difficult problem of retrieving the legal provisions of wildlife protection law, aiming at building an efficient and intelligent retrieval system. By integrating knowledge map and natural language processing technology, the system architecture is carefully designed, covering data acquisition and preprocessing, knowledge map construction, natural language processing, retrieval and user interface modules. In this way, we focus on building a core algorithm based on semantic vector similarity and knowledge map structure information. In the experiment, the data sets of laws and regulations on wildlife protection in many regions were selected and compared with traditional retrieval methods. The results show that when the number of queries is 100, the proportion of the correlation score of the fusion model is above 80 points is 75%, the retrieval takes 0.8 seconds, and the coverage of the retrieval results is 83%, which are significantly better than the BM25 method based on keyword matching and the SVM method based on simple semantic vector space model. The research shows that the fusion model has obvious advantages in the quality, efficiency and comprehensiveness of retrieval results, and provides a better solution for the retrieval of legal provisions of wildlife protection laws.