In order to effectively identify students′ online classroom action,a h uman skeleton action recognition model integrating global attention mechanism and spatiotempor al convolution network is proposed.Firstly,a global attention module is added bet ween the spatial graph convolutional network and the temporal convolutional network of the Spatio temporal graph convolutional neural network,and the spatial feature map output by the sp atial graph convolutional network is used as the input of the attention module;Secondly,av erage pooling and maximum pooling operations according to the time dimension are introduced to increase the ability of the model to learn global feature information.Finally,three spa tiotemporal graph convolutional neural networks and class activation map (CAM) added to the atten tion mechanism are used to construct a rich activation map convolutional network with stronger ability to recognize occlusion data (RA-GCNv2-A) model,and realize student onl ine classroom action recognition function through transfer learning.Experimental ve rification is performed on the NTU-RGB+D and NTU-RGB+D120two datasets.Compared with the RA - GCNv2model,the recognition accuracy on the NTU-RGB+D dataset is increased by 1.3% (cross-subject,CS),1.2% (cross-view,CV),the recognition accuracy on the NTU-RGB+D120dataset is increas ed by 1.6% (cross-subject,CSub),1.4% (cross-setup,CSet) respectively.The experimental results show that the pr oposed method is an effective way to recognize students′ online classroom action.