In the digital wave, user behavior prediction has become a core tool for enterprises in precise marketing, product optimization and risk prevention and control. This article starts from the prediction logic driven by big data and systematically analyzes the core principles of user behavior prediction, including key links such as data collection, feature engineering, model construction and optimization. By comparing traditional prediction methods, the advantages of technologies such as deep learning and time series analysis in capturing the dynamic characteristics of user behavior are revealed. It also delves into cutting-edge directions such as data quality governance, multimodal data fusion, and model lightweighting, and proposes a balanced strategy for data privacy protection and model interpretability. Research shows that big data-driven user behavior prediction significantly enhances prediction accuracy and commercial value by integrating multi-source heterogeneous data and establishing a dynamic feedback mechanism, providing key technical support for digital transformation.
Shuming Shi (Sat,) studied this question.