Research on Online Learner Behavioral Segmentation Based on the Improved K-means + RFM Algorithm Model

Online learning has emerged as a widely accepted learning method. With the explosive growth of Internet educational resources, the amount of user data continues to grow geometrically. However, there is an urgent need to address issues of extracting value from platform data, promoting the utilization of educational resources, optimizing teaching strategies, and adjusting and deepening platform teaching work. Therefore, this paper employs an improved K-Means algorithm and a modified RFM model to comprehensively analyze online learner data, providing technical support for constructing course systems and their practical paths.