Aiming at the problem of insufficient representation of interactive semantic information in the double human interaction behavior recognition method based on graph convolutional neural networks，a new double human interactive spatial?temporal graph convolution network (DHI?STGCN) was proposed for behavior recognition. The network contains spatial sub?network modules and temporal sub?network modules. Based on the 3D skeleton data obtained from the interactive action video, a spatial action graph of double human interactive action was generated for the representation of spatial information. In the graph, the connecting edges between double human were given different weights according to the joint point position information. The connection of context time information was added in the constructed adjacency matrix, and the joint points in the graph were connected with their nodes within a certain time range in time information processing. The generated spatial?temporal graph data was sent to the spatial graph convolution network module, and the temporal graph convolution network module was combined to enhance the continuity of inter frame motion features for modeling in time. The model fully considers the close relationship of double human interaction. The comparative experimental results on NTU?RGB+D dataset show that the algorithm has strong robustness and obtains better interaction recognition effect than the existing models.