Classroom Behavior Recognition and Analysis Technology Based on CNN Algorithm

Students’ classroom behavior can effectively reflect the learning efficiency and the teaching quality of teachers, but the accuracy of current students’ classroom behavior identification methods is not high. Aiming at this research gap, an improved algorithm based on multi-task learning cascaded convolutional neural network architecture is proposed. Through the improved algorithm, a face recognition model is constructed to identify students' classroom behavior more accurately. In the performance comparison experiment of the improved convolutional network algorithm, it was found that the recall rate of the improved algorithm was 88.8%, higher than the three comparison models. The result demonstrated that the improved algorithm performed better than the contrast model. In the empirical analysis of the face recognition model based on the improved algorithm, it was found that the accuracy of the proposed face recognition model was 90.2%, which was higher than the traditional face recognition model. The findings indicate that the model developed in this study is capable of accurately reflecting the students' state in the classroom, thereby facilitating the formulation of targeted teaching strategies to enhance their classroom efficiency.

@article{Qiao2025,
  title     = {Classroom Behavior Recognition and Analysis Technology Based on CNN Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Weihua Qiao},
  doi       = {10.14569/IJACSA.2025.0160347},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160347}
}