Objective Existing student behavior recognition models suffer from low detection accuracy and insufficient prediction speed. To address this this paper proposes a smart classroom behavior recognition algorithm based on the YOLOv7-SSW model aiming to improve detection accuracy while ensuring real-time performance. Methods First the Squeeze-and-Excitation SE attention mechanism was integrated into the backbone network of YOLOv7. This integration strengthened the modeling of inter-channel correlations and improved feature sensitivity thereby improving the detection accuracy of the algorithm. Second to enhance real-time performance the original serial-channel Spatial Pyramid Pooling with Cross Stage Partial Connections Structure SPPCSPS module was redesigned into a parallel architecture significantly accelerating the detection speed. Finally the Wise-IoU loss function was integrated to strengthen the algorithm?? s capability in handling low-quality samples in training data further boosting detection accuracy. Results The proposed method achieved a mean Average Precision mAP of 91. 9% on the STBD-08 dataset representing improvements of 4. 4% and 5. 6% over the CBPH-Net and YOLOv7 algorithms respectively. The single-frame inference time was only 45. 1 ms. Conclusion The proposed algorithm enables real-time and accurate recognition of student classroom behaviors providing technical support for transforming ideological and political education from knowledge indoctrination to behavioral literacy cultivation. It holds significant importance for advancing digital innovation and development in education.