The purpose of this study is to build a case-based reasoning (CBR) theoretical Intelligent Question Answering System Based on large language model. By integrating artificial intelligence and natural language processing technology, the effectiveness and advantages of the system in solving complex problems and enhancing user interaction experience are explored. The study mainly uses large language model to build data sets for model training and carefully designs the system structure to support efficient question answering processing and case-based reasoning mechanism. After receiving user questions, the system accurately extracts relevant cases from the database with the help of semantic matching and case retrieval technology, and generates answers through intelligent reasoning, so as to provide users with accurate and insightful analysis and suggestions. At the same time, the impact of different language models on the system performance is further analyzed, and targeted optimization strategies are proposed to continuously improve the reasoning ability of the system for various complex problems, provide a new perspective and practical support for the development of intelligent question answering system, and lay a solid foundation for its wide application in the fields of education, consulting and technical support in the future.