Research on the Application of Statistical Methods in the Classification of E-Commerce User Purchase Behavior

This study systematically reviews the application of statistical methods in the classification of e-commerce user purchase behavior, aiming to provide theoretical and practical guidance for the intelligent development of e-commerce platforms. Starting from the definition and characteristics of user purchase behavior, the study analyzes its dynamics, multidimensionality and scenario dependence, and clarifies the statistical basis of classification problems and the characteristics and challenges of e-commerce data. Traditional statistical methods such as logistic regression and cluster analysis are based on their simplicity and interpretability, and are suitable for simple scenarios, but are limited in processing high-dimensional and sparse data. The combination of machine learning and statistical methods, such as random forests and neural networks, significantly improves classification accuracy and the ability to model nonlinear relationships. Model evaluation and optimization methods, such as cross-validation and feature selection, further ensure the robustness of the model. By sorting out the applicable scenarios and limitations of the methods, this study reveals the core role of statistical methods in precision marketing and personalized recommendations, and looks forward to its application prospects in real-time recommendations and privacy protection, providing a reference for the intelligent transformation and interdisciplinary research of the e-commerce industry.