Skeleton-based human action recognition is a research hotspot in recent years, but most of the research focuses on the spatio-temporal feature extraction by convolutional neural network. In order to improve the correct recognition rate of these models, this paper proposes three strategies: using algebraic method to reduce redundant video frames, adding auxiliary edges into the joint adjacency graph to improve the skeleton graph structure, and adding some virtual classes to disperse the error recognition rate. Experimental results on NTU-RGB-D60, NTU-RGB-D120 and Kinetics Skeleton 400 databases show that the proposed strategy can effectively improve the accuracy of the original algorithm.