Research on Customers' Purchasing Intention of Sports Shoes Based on Data Mining Application

The rapid development of e-commerce makes online shopping the main means of social consumption. How to extract useful information from massive semi-structured and unstructured customer data has become a hot issue in e-commerce industry research. This article uses the Octopus data crawler software to capture the online purchase information of Jingdong Mall sports shoes customers, and classify and analyze the information, and analyze the price, color, size, brand, etc. of the most popular sports shoes purchased by consumers; On the other hand, GeNie is used to generate online reviews of the Bayesian network structure model for the platform, and the cross-validation method is used to test the accuracy of the model. Finally, the key factors affecting the satisfaction of the sneaker customers are analyzed, and the customer satisfaction is studied. There is a significant causal relationship between the target variable and other related variables, and there are different degrees of correlation between different variables. The most important factor for consumers to purchase sports shoes is the comfort, quality and brand of sports shoes.