## 5 Discussion  
Aiming at the education in the synchronous classroom environment and combining with the current popular deep learning methods, this paper adopts the methods of object detection and image recognition to achieve the purpose of intelligent classroom behavior recognition and automatically analyzes the recognition results. The experimental results show that the deep learning method can quickly identify classroom pictures with an accuracy of 77.2%, and has great potential for improvement. This paper provides a reference case for the combination of deep learning and classroom teaching analysis. However, from the practice of this paper, in order to realize the application of automatic recognition and analysis of classroom teaching behavior, there are still the following problems to be solved:  
(1) Video data quality is an important factor affecting the performance of classroom teaching behavior recognition. The first is video data recording standards and the es  
Figure 14: Changes of class engagement. Figure 15: Spatial distribution of students’ phased engagement in Class 30.  
tablishment of a basic behavior database. In the process of education informatization, various smart classrooms have been established in different places, but the video recording resolution and camera distribution of these classrooms are different. The density and occlusion of students will also seriously affect the recognition performance. According to the research in this paper, to support the recognition function of deep learning, it is better to have a resolution above 4k so that a higher recognition rate can be achieved when the number of students in class is large. The only data in this paper are videos from the perspective of students, but to achieve complete teaching analysis, it is better to have complete videos from the perspective of students and teachers. The second is the integrity of the data collection. Most classroom teaching behaviors are verbal behaviors, while only nonverbal body behaviors are used in this paper. Voice and speech information, which play an important role in teaching, are not used, and information loss will seriously affect the behavior recognition rate. (2) Artificial intelligence recognition technology needs to be further enriched and improved. Technically speaking, video-based body behavior recognition has been able to achieve a certain effect, but classroom teaching is a dynamic process, and its behavior before and after has a causal sequence connection. Therefore, in the following classroom teaching recognition research, contextual information and timing information should be fully used so as to further improve the recognition effect under the condition of simplifying the rules. In addition, the performance of classroom behavior recognition based on deep learning still has great room for improvement. (3) Classroom teaching behavior classification and coding rules are the basis for automatic recognition and analysis of classroom teaching behavior. The teaching behavior classification table adopted in this paper is adapted from the classical teaching behavior classification table. However, for many classroom teaching behavior analysis tasks, the behavior and action categories of this classification table are insufficient. In the field of artificial intelligence, public behavior standards and behavior classification tables should be established as far as possible. Different from standardized data collection in the field of artificial intelligence, classroom teaching behavior analysis has various objectives and forms. Therefore, it is necessary to study and establish a targeted classification table of teaching behavior according to different objectives of teaching behavior analysis. In addition, on the basis of the classification of teaching behaviors, it is also necessary to study the mapping relationship between body movements and teaching behaviors in classroom teaching so as to establish a theoretical basis for automatic recognition and discrimination of classroom behaviors.