Recently, the research on skeleton-based action recognition has attracted a lot of attention. As the graph convolutional networks can better model the internal dependencies of non-regular data, the spatio-temporal graph convolutional network (ST-GCN) has become the preferred network framework in this field. However, most of the current improvement methods based on the ST-GCN framework ignore the geometric features contained in the skeleton sequences. In this study, we exploit the geometric features of the skeleton joint as the feature enhancement of the ST-GCN framework, which has the advantage of visual invariance without additional parameters. Further, we integrate the geometric feature of the skeleton joint with earlier features to develop ST-GCN with geometric features. Finally, the experimental results show that the proposed framework achieves higher accuracy on both NTU-RGB+D dataset and NTU-RGB+D 120 dataset than other action recognition models such as ST-GCN, 2s-AGCN, and SGN.