Abstract:

The research of tourism flow network structure based on big data mining is one of the main directions of advanced tourism flow research. Using web crawler technology, this study captured the tourism flow data of five major urban agglomerations in coastal China from Ctrip, then examined the spatial structure of tourism flow network, and analyzed the spatial network structure characteristics of tourism flow from the aspects of network density, node structure, network aggregation subgroups, core-periphery characteristics, and structural holes. The results show that: Firstly, the tourism flow network structure of the five major urban agglomerations in coastal China has hierarchical and rank-size characteristics. Secondly, the cohesive subgroups are closely related to each other, but there is little interaction among them. Thirdly, the core areas are all located in the Yangtze River Delta urban agglomeration, and the "trickling down effect" of the core nodes on the peripheral nodes is limited. Lastly, Beijing, Xiamen, Qingdao, Guangzhou, Zhongshan, and Hangzhou have obvious advantages in the tourism flow network. Revealing the characteristics of spatial network structure of tourism flow using big data is of great significance for further understanding the connotation of flow space and optimizing the spatial layout of urban tourism.

Key words: tourism flow, network structure, big data mining, social network analysis, five major urban agglomerations of coastal China