In order to solve the problem that the spatial structure and temporal dynamic structure of skeletal data are not clearly and fully utilized when using hand bone data for action recognition, a spatio-temporal synchronous graph convolution network with combined attention mechanism is designed. Using the method of 2D estimation and triangulation, the feature is projected into a single 3D volume, the 3D heat map is output, and the 3D joint coordinates are obtained by soft-argmax operation on the heat map. The spatial dimension and the temporal dimension of the bone data are separated, the spatial dimension is encoded according to the order of the related nodes, and the same related nodes are encoded in the temporal dimension, and the spatial embedding matrix and the temporal embedding matrix are obtained. The matrix is synchronously added to the spatio-temporal network sequence. The experiment is tested on the SHREC2017 data set, and compared with some representative gesture recognition methods, the results show that the algorithm has achieved good results in hand action recognition.