Recognition of students' abnormal behaviors in English learning and analysis of psychological stress based on deep learning.

The recognition of students' learning behavior is an important method to grasp the changes of students' psychological characteristics, correct students' good learning behavior, and improve students' learning efficiency. Therefore, an automatic recognition method of students' behavior in English classroom based on deep learning model is proposed. The deep learning model is mainly applied to the processing of English classroom video data. The research results show that the video data processing model proposed in this paper has no significant difference between the data obtained from the recognition of students' positive and negative behaviors and the real statistical data, but the recognition efficiency has been significantly improved. In addition, in order to verify the recognition effect of the deep learning model in the real English classroom environment, the statistical results of 100 recognition result maps are compared with the results of manual marking, and the average recognition accuracy of 100 recognition effect maps is finally obtained, which is 87.33%. It can be concluded that the learning behavior recognition model proposed in this paper has a high accuracy and meets the needs of daily teaching. It further verifies that the developed behavior recognition model can be used to detect students' behavior in English class, which is very helpful to analyze students' psychological state and improve learning efficiency.