Abstract: ‍ ‍At present， the most common skeleton action recognition methods adopt a joint stream， bone stream， and corresponding motion stream as multi-stream networks for separate training operations， which results in high training costs. In addition， the modeling of complex spatio-temporal dependencies is neglected in the feature extraction process， and large-scale convolution is adopted for the exchange of information in the temporal domain， leading to the aggregation of a large amount of redundant information. A space-time-correlated transformer skeleton action recognition method was investigated to address these problems. First， a motion fusion module was constructed to reduce the cost of training motion streams separately by using joint and skeletal streams as inputs and fusing the respective motion information at the feature level. Second， a shift transformer module was proposed， which used the characteristics of the temporal shift operation to mix spatio-temporal information with the transformer to capture the short-term spatio-temporal dependencies at a low cost. Then， a multiscale temporal convolution was designed for time-domain long-term information. Finally， the final classification prediction was obtained by fusing the two-stream scores. Experiments on the large-scale datasets NTU RGB+D and NTU RGB+D 120 showed that the model achieved recognition accuracies of 91.5% and 96.3% on the two evaluation standards X-Sub and X-View for the NTU RGB+D dataset， respectively； and 87.2% and 89.3% on the two evaluation standards X-Sub and X-Set for the NTU RGB+D 120 dataset， respectively. The recognition accuracy of the proposed method was significantly better than those of the most commonly used skeleton action recognition methods， which verified the effectiveness and generality of the model.