Analysis of Classroom Teaching Interaction Patterns Based on Behavior Recognition

The development of intelligent education has at-tracted wide attention and plays an increasingly important role in primary and secondary education. However, the practical application of classroom behavior recognition remains chal-lenging due to limitations in real classroom environments and insufficient utilization of recognition results. This leads to a gap in understanding classroom interactions. To address this problem, we propose a behavior recognition-based framework for analyzing classroom teaching interaction patterns. By noticing the different behaviors of teachers and students and the interactions between them in classroom videos, we build a framework that identifies these behaviors and analyzes their interaction patterns. Our approach achieves significant results on a large number of classroom videos, extracting typical interaction patterns and demonstrating the effectiveness of the framework. In the future, enhancing behavior recognition accuracy and expanding video analysis are essential for a deeper exploration of classroom in-teractions, thereby further supporting the digital transformation of education.