In order to alleviate the problem of knowledge dispersion and information fragmentation in the tourism domain, an entity relation extraction model ChatGLM-ppt (fine-tuning ChatGLM with prompt and p-tuning) based on ChatGLM (chat generative language model) and prompt fine-tuning is proposed. The model accomplishes the entity and relation extraction task in the form of dialog with the help of ChatGLM, and copes with the problems of error propagation, entity redundancy and relationship overlapping in entity and relation extraction by means of P-Tuning v2 fine-tuning and adding prompt templates. The experiments are built on the self-constructed tourism domain dataset, and the results show that the F1 value of the ChatGLM-ppt model is 92.19% in the entity and relation extractio n problem in the tourism domain, and the F1 values are all greater than 90% in dealing with overlapping relationships, which is better than that of the current mainstream entity and relation extraction models, proving that the model can effectively improve the accuracy of entity and relation extraction. Further use of Neo4j graph database to construct tourism knowledge graph and integrate dispersed tourism information resources has certain reference significance for promoting the digital transformation and intelligent development of tourism.