Research on Big Data Analysis of E-commerce User Behavior Based on Spark

Under the background of digital economy, e-commerce platform, as the core driving force of e-commerce industry, has gradually evolved into a comprehensive application system integrating marketing, commodity trading, customer service and in-depth data analysis, greatly enriching consumers' shopping experience. However, in the face of the continuous expansion of user groups and transaction scale, the data processing and analysis methods used in the e-commerce platform have obvious limitations, and it is difficult to realize real-time and accurate processing of user behavior data. In this regard, based on the actual needs, this study will deeply explore the feasibility of applying big data analysis technology in e-commerce platform, and put forward a brand-new e-commerce user behavior data analysis model combined with Spark framework to reveal the deep-seated patterns and trends of user behavior and provide strong data support for e-commerce platform decision-making. Practice has proved that the real-time acquisition and processing of user behavior data are realized by using Spark Streaming component. XGboost prediction model based on Spark MLlib component has high accuracy and stability in predicting user behavior, which is beneficial to improving personalized recommendation effect and user satisfaction of e-commerce platform, and can meet the ever-changing e-commerce market and user needs.