For solving the separation problem among student behavior recognition, engagement evaluation and teaching intervention in university classrooms, this article puts forward a method of classroom behavior analysis and individualized teaching intervention which is based on multimodal deep learning. By integrating classroom video, audio, pose sequences, and instructional contextual information, we establish a unified multimodal data organization framework and design the Behavior-State-Intervention Progressive Coupling Network (BSIC-Net), introducing targeted optimizations in three key aspects: reliability-weighted fusion, state-coupled modeling, and intervention prioritization.Based on a cross-domain experimental protocol, we conducted validation of behavior recognition, engagement state estimation, and intervention prioritization on the SCBD, SAV, OUC-CGE, CMOSE, and DIPSER datasets.The results show that the proposed method achieves core metrics of 87.8%, 85.9%, 81.7%, 84.8%, and 80.6% across the five datasets, respectively, with an average performance 3.52 percentage points better than the strongest baseline, and an ECE reduced to 0.043.In the intervention sequencing experiments, the model achieved a 13.0% recovery in engagement by the sixth round for the high-risk student group, representing a 3.6 percentage point improvement over empirical strategies. The ablation experiment outcomes indicate that reliability weight assignment, the state connecting layer, and the intervention sorting head directly make contributions to the main task accuracy degree, state distinguishing stability, and sorting quality, in respective order. This study demonstrates that multimodal classroom analysis can be further advanced from behavior recognition to decision support for instructional responses, providing a practical technical pathway for real-time intervention and refined instructional management in university classrooms.
