Action recognition is one of the basic tasks of computer vision. The skeleton sequence contains most of the action information, so skeleton-based action recognition has attracted a lot of research attention. Mathematically, the human skeleton is a natural graph, so graph convolution is widely used in action recognition. But ordinary graph convolution only aggregates low-order information between pairwise nodes, and cannot model high-order complex relationships between multiple nodes. To solve this problem, a multiscale hypergraph convolutional network is proposed, which aggregates richer information in the two dimensions of space and time, so as to improve the accuracy of action recognition. The multiscale hypergraph convolutional network has an encoder-decoder structure. The encoder uses the hypergraph convolution module to aggregate relevant information between multiple nodes in the hyperedge, and the decoder uses the hypergraph fusion module to restore the original skeleton structure. In addition, a multiscale temporal graph convolution model based on dilated convolution is designed, which is used to better aggregate the temporal-dimension motion information. The experimental results on NTU-RGB+D and Kinetics datasets verify the effectiveness of this algorithm.