Research on the Development of China’s Sports Tourism Industry Based on the Background of Big Data Era

Abstract

In this paper, we use the support vector regression algorithm to minimize the loss function between the real value of sports tourism samples and the predicted value of the model, deal with the complex nonlinear problems through the MLP neural network model, and design a sports tourism development model based on the machine learning model. Focusing on the use of a support vector regression algorithm and MLP neural network model to predict the tourist flow, the panel threshold regression model is used to test whether the variables have a threshold effect on each other and the relationship between the sports industry and tourism development is deeply explored. Based on this basis, the current situation and development of sports tourism were analyzed using data from Province S and 29 provinces in China. The results show that there is a threshold effect on the influence of the Chinese sports industry on regional tourism development, and the estimated value of the threshold is 1.8293. The level of the sports industry in Chinese provinces can be divided into less developed regions of the sports industry ≤1.8293 and developed regions of the sports industry >1.8293. In the first model, which only includes the value of the level of the sports industry in the current period, the relationship between the sports industry and regional tourism development presents a “ λ Λ” shape.

Keywords

• support vector regression
• mlp
• panel threshold regression
• machine learning
• sports tourism industry
• 68p01