Abstract: Traditional methods of classroom quality evaluation mainly rely on manual observation, which suffers from low efficiency and poor accuracy. To establish a more comprehensive evaluation system, a lightweight classroom evaluation model based on an improved YOLOv5s(You Only Look Once version 5 small) is proposed. By adopting this model and the AHP(Analytic Hierarchy Process), a comprehensive classroom evaluation system is established. The model integrates the CBAM(Convolutional Block Attention Module) attention mechanism into the neck network, enhancing the model’s recognition accuracy. incorporates the Ghost module into the backbone network, significantly reducing the model’s complexity. and utilizes the Focal Loss function to effectively mitigate the problem of class imbalance. Experimental results show that, compared to the YOLOv5s model, the improved model increases average precision by 7. 3%, reduces the number of parameters by 42. 0%, decreases computation by 33. 1%, and improves detection speed by 4%. Finally, a classroom quality evaluation system is established by combining the AHP and the entropy weight method, dynamically displaying the current classroom quality score, which meets the actual needs of the classroom.

Key words: you only look once version 5 small(YOLOv5s), analytic hierarchy process (AHP), entropy weight, convolutional block attention module (CBAM), Ghost module, Focal Loss function