Aiming at the problem of multi-person wearing masks in the classroom and gesture recognition in COVID-19, this paper presents a multi-person state detection algorithm, based on the YOLO and OpenPose models. The Efficient-YOLO model proposed in this paper uses the classical CBAM attention and SPNET-NEW modules to deal with the problems of multi-person occlusion and irregular targets. In addition, this paper presents a lightweight Class-OpenPose model to detect the students’ posture. Based on the OpenPose model, our proposed algorithm uses ShuffleNetV2-NEW to improve the traditional model in terms of low-level feature extraction, and extracts correct key posture points in complex environments and in real-time. Experiments show that in the multi-person abnormal event detection task, the average accuracy of the Class-OpenPose model is 79. 0% that is higher than that of the traditional model, and the detection speed reaches 13. 5 F/ s; the Efficient-YOLO mask recognition model achieves an average accuracy of 83. 1%, and the detection time is only 31. 54 ms, which provides a good algorithm idea for classroom student status detection.