Intelligent Stable Monitoring Algorithm of Students' Online Learning Behavior Based on User Portrait

In the monitoring of students’ online learning behavior, the accurate capture rate of learning behavior has been low due to the complex actions of students. An intelligent monitoring algorithm of students’ online learning behavior based on user portraits is designed. This paper collects student data from five aspects: demographic attributes, academic attributes, behavior habits, interests and hobbies, and psychological attributes, and constructs student user portraits. The deep perception model of online learning behavior is designed from two levels of face recognition and emotion perception to implement the deep perception of students’ online learning behavior. According to the perception results, the online learning behavior is monitored intelligently through three stages: before learning, during learning and after learning, so as to realize the intelligent monitoring of students’ online learning behavior. The algorithm is tested. The test results show that the designed algorithm has a high accurate capture rate for learning behavior of all grades, and the designed algorithm has a high learning efficiency and a good learning effect.