Learning behavior analysis and personalized recommendation system of online education platform based on machine learning

With the rapid development of Internet technology, online education platforms are booming. Online education breaks the time and space limitations of traditional education and attracts a large number of learners. However, in the face of massive learning resources and the different needs of many learners, how to effectively analyze learning behaviors and provide personalized recommendations has become an urgent problem to be solved. This study focuses on the application of machine learning technologies in online education platforms. A learning behavior analysis model is constructed using a machine learning algorithm by collecting a large amount of learning behavior data from online education platforms, such as learning duration, frequency of course visits, homework completion, interaction records, etc. The model can deeply explore the characteristics of learners' learning habits, preferences, and learning abilities. The experimental results show that the system is highly accurate in learning behavior analysis. For example, the accuracy rate reached more than 70 % in predicting learners' preference for specific course types. At the same time, the personalized recommendation system recommends appropriate courses and learning materials for learners according to the results of the analysis, which significantly improves learners' participation. The data shows that the course completion rate of learners who receive personalized recommendations is about 30 % higher than that of learners who do not. The learning time is also significantly increased. This shows that machine learning technology has great potential in learning behavior analysis and personalized recommendation of online education platforms, which can significantly improve the teaching effect of online education and learners' learning experiences.