Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms

摘要

In the context of economic globalization, the rapid transmission of network information, people's attention to tourism culture perspective has changed significantly; new and popular tourism projects are more favored. In order to avoid large-scale crowding and waste of resources in tourist attractions, it is a hot topic in the field of tourism to study the spatial and temporal distribution of tourist flows. By analyzing the spatial and temporal distribution characteristics of tourist flow in scenic spots, this paper constructs a big data platform based on tourist flow information, and proposes a data mining technology based on the DA-HKRVM algorithm to predict the tourist flow in the dimension of spatial and temporal distribution. By feeding the forecast results back to the staff of scenic spots in real time, the scale of passenger flow distribution can be effectively controlled, the purpose of balanced distribution of tourism resources can be achieved, and the development of intelligent tourism can be further promoted. The simulation result shows that the spatial-temporal distribution model of tourist flow based on data mining has good adaptability and accuracy in application. It shows that the method proposed in this paper can reduce the negative impact caused by the uneven spatial and temporal distribution of tourism flow, and can provide theoretical guidance for the efficient development of tourism economy.