Complexity of tourism products exists in route design and service combination provided by suppliers. Generating reasonable rules by utilizing large-scale textual data of tourism products will be an effective way to explore imitation and competition from product-product relationships thus observing how they respond to consumer demands and market changeable. In this sense, constructing a tourism product knowledge graph (TPKG) will be a key data strategy for a travel agency or e-commerce platform. This paper constructs a knowledge graph of tourism products with seven feature dimensions and creates a structured model to imagine the service details and features. BiLSTM-CRF was used to extract entities from large-scale textual data, while entity-related attributes and attribute values were extracted from Baidu Baike. Furthermore, possibility of correlation between entities using the link prediction algorithm was checked, and entity disambiguation was accomplished. Finally, TPKG was visualized using Neo4j graph to show the validity of ontological structure for tourism products. Our paper provides a new process and method to uncover the imitation and competition relationship among tourism products from a nuanced particle perspective.