Online education is a network-based approach to teaching. It is a method of content dissemination and rapid learning through the application of UGC and Internet technology.Compared with traditional school education, online learning can obtain more resources, more autonomy, and no longer limited time and space for learning.Through the questionnaire, this paper finds that learners in the online education model still have some shortcomings in the learning process.For example, the learning process is not durable.Therefore, this paper uses neural network classification algorithm to analyze the related factors that affect the learning behavior of online education students.And propose corresponding control strategies for different influencing factors.By constructing a learning process control strategy model for large educational data, to help learners improve their learning efficiency, help the online education model break through the bottleneck, the online education industry has maintained rapid development.Finally, through the comparative analysis of the improved online education model and the traditional online education model, finding an improved online education model can better improve students' interest in learning.Provided a reference for the development of online education,It also provides a reference for the transformation and upgrading of traditional education to online education.