Classification and Prediction of User Churn Risk in Live-Streaming E-Commerce Based on Machine Learning Algorithms

The popularization of encryption protocols such as HTTPS and Tor not only significantly enhances the privacy protection capability of network traffic, but also provides a hidden space for risks such as the spread of malicious websites and the leakage of sensitive information. The transmission characteristics of the current diverse network environments such as home WiFi, public WiFi, 4G and 5G are significantly different, which makes encrypted traffic exhibit cross-environment variation features such as delay fluctuations, retransmission rate changes and uneven data packet distribution, posing a severe challenge to the generalization and accuracy of the fine classification of encrypted traffic in cross-network environments. To solve this difficult problem, this paper proposes an LSTM algorithm optimized based on the multi-head attention mechanism and introduces a large model to assist in summary analysis and result output. The research first conducted correlation analysis and violin graph analysis, and then carried out comparative experiments through multiple machine learning algorithms. After verification by the test set, the final results were presented by the large model. Experiments show that the proposed algorithm demonstrates absolute advantages in all evaluation indicators. The accuracy rate, recall rate, precision rate, F1 value all reach 99%, and the AUC value is as high as 100%, comprehensively surpassing all comparison algorithms. The application of large models has effectively enhanced the efficiency of analysis and the credibility of results. This research provides reliable technical support for the precise and detailed classification of encrypted traffic in cross-network environments and has significant practical significance for network security protection and risk control.