At present, most of the research on students’ classroom learning status focus on single-person online monitoring, and the monitoring of offline classroom with multiple students and complex environment is still in the exploratory stage. A monitoring system for students’ classroom learning status was designed for offline education to monitor students’ classroom attendance and fatigue state of students’ faces in real time. First, DSFD face detection algorithm combined with ResNet deep residual network was used to recognize students’ faces and record students’ attendance. Then, ERT regression tree set algorithm combined with head pose estimation was used to detect the fatigue behavior of yawning and drowsiness. Then, the improved YOLOv5 object detection algorithm added CBAM module was used to detect students’ closed eyes behavior. Finally, a complete set of integrated attendance, fatigue detection of student classroom learning state monitoring system is formed. In the actual classroom test environment, the system can accurately calculate the students’ attendance, and can real-time monitor the fatigue state of yawning, lower head and closed eyes on the face of the students. The detection accuracy rate is more than 90%, and the detection speed is about 14.1 fps, which proves that the system has important use value.