LAUSR

With the vigorous development of the Chinese economy and people's pursuit of quality, sports activities of people pursuit are no longer limited to simple physical exercise, but a way that pursues higher-quality sports tourism. As a new industry, it cannot guarantee that sports tourism will be accepted by all people, and it will be limited by geographical, economic, time, and other conditions. The participation number of Chinese sports tourism is more concerned by organizers or operators. Predicting the participation number of sports tourism based on the knowledge discovery method is meaningful and economical work. In this paper, a variety of sports tourism data are classified by clustering method, and the categories with similar characteristics are classified. Then, the convolution and long short-term memory hybrid neural network are used to extract the spatial and temporal information of sports tourism characteristics, which completes the prediction of Chinese sports tourism categories. The research results show that the clustering method has high accuracy for the classification of sports tourism categories, and the weights occupied by the categories are relatively uniform. The ConvLSTM neural network also has obvious advantages in predicting Chinese sports tourism methods. The largest error is only 2.89%, and the correlation coefficient also reaches 0.98, which is enough to be trusted for the prediction of Chinese sports tourism categories.