Multi-feature fusion model for interactive behaviour recognition in university classrooms using convolutional neural networks and temporal attention mechanisms

This study proposes a multi-feature fusion model combining convolutional neural networks (CNN) for spatial feature extraction and temporal attention mechanisms for dynamic behaviour modelling in university classrooms.The model was trained and evaluated on a dataset of 5,000 annotated classroom behaviour samples collected from intelligent classrooms across multiple disciplines.Compared to baseline methods such as C3D and CNN-LSTM, the proposed model achieves an F1-score improvement of 5%-9% and increases recognition accuracy to over 90% for key interactive behaviours including hand raising, standing, and note-taking.These results demonstrate the effectiveness of integrating spatial and temporal features for precise classroom behaviour recognition, providing quantitative support for intelligent classroom analysis without relying on subjective evaluation.