There are many algorithms for behavior detection in different datasets, but there is a little lack of algorithms for student behavior detection in classroom. In order to achieve better accuracy and real-time of student behavior detection, this paper improves the network structure based on MTCNN, and proposes a new activation function and a loss function to detect student images and landmark localization. Meanwhile, this paper proposes the strategy of joint classification of student behaviors through the image classification network and the landmark localization classification network. The experimental results show that the proposed improvement actions effectively improve the accuracy of student behavior detection and the final detection accuracy of the model is 78.6%. On the embedded development board of Jetson TX2, the proposed algorithm has the real-time detection accuracy and speed superior to those of the other algorithms such as YOLOv3 and SSD.