An AI large model-assisted smart tourism tour guide system based on WeChat mini-programs and Spring Boot

With the rapid development of artificial intelligence technology, the field of smart tourism is undergoing an unprecedented transformation. This paper proposes an AI large model-assisted smart tourism tour guide and knowledge enhancement system based on WeChat mini-programs and Spring Boot. This system is designed to provide users with immersive and personalized travel experiences through multimodal interaction, personalized recommendations and real-time information updates. The system adopts the Spring Boot framework to build the back-end service, combines the WeChat mini-program to achieve the front-end interaction, and uses the DeepSeek-R1 large model for intelligent navigation and knowledge generation. By optimizing the large model through LoRA and RAG technologies, the system can achieve efficient parameter fine-tuning and knowledge retrieval, thereby enhancing the accuracy and real-time performance of guided Tours. In addition, the system also supports multi-language interaction and social sharing functions, further enhancing the user experience. This paper details the architecture design of the system, the implementation of key technologies and the test results, verifying its effectiveness and feasibility in the smart tourism scenario. This system not only provides tourists with convenient guided tour services, but also offers new technical means for scenic area management and cultural dissemination, and has broad application prospects.