Gait　has　unique　physiological　characteristics　and　supports　long-distance　recognition,　so　gait　recognition　is　ideal　for　areas　such　as　home　security　and　identity　detection.　Methods　using　graph　convolutional　networks　usually　extract　features　in　the　spatial　and　temporal　dimensions　by　stacking　GCNs　and　TCNs,　but　different　joints　are　interconnected　at　different　moments,　so　splitting　the　spatial　and　temporal　dimensions　can　cause　the　loss　of　gait　information.　Focus　on　this　problem,　we　propose　a　gait　recognition　network,　Multi-scale　Spatio-Temporal　Gait　(MST-Gait),　which　can　learn　multi-scale　gait　information　simultaneously　from　spatial　and　temporal　dimensions.　We　design　a　multi-scale　spatio-temporal　groups　Transformer　(MSTGT)　to　model　the　correlation　of　intra-frame　and　inter-frame　joints　simultaneously.　And　a　multi-scale　segmentation　strategy　is　designed　to　capture　the　periodic　and　local　features　of　the　gait.　To　fully　exploit　the　temporal　information　of　gait　motion,　we　design　a　fusion　temporal　convolution　(FTC)　to　aggregate　temporal　information　at　different　scales　and　motion　information.　Experiments　on　the　popular　CASIA-B　gait　dataset　and　OUMVLP-Pose　dataset　show　that　our　method　outperforms　most　existing　skeleton-based　methods,　verifying　the　effectiveness　of　the　proposed　modules.