A gait recognition method based on multi-feature fusion using Frequency-Modulated Continuous Wave （FMCW） radar was developed to overcome the low recognition accuracy of the existing millimeter-wave point cloud gait recognition methods caused by relying on static spatial features excessively and lacking modeling of dynamic motion information. First， the three-dimensional coordinates of the point cloud were aligned spatially to obtain a unified spatial representation. On this basis， the velocity and signal-to-noise ratio features of target points were fused， and the spatial structure and dynamic information were modeled jointly. Second， cross-spatio-temporal gait features were further extracted by multi-scale convolution and pooling modules. Experimental results show that the proposed method achieves high recognition accuracy on multiple public datasets and a self-built dataset， with an average accuracy of 86.3% and a maximum accuracy of 92.2%. Across different scenarios， it has the recognition accuracy improved by 0.90% to 32.08% compared with comparative methods such as mmGaitNet and PointMLP. It can be seen that through multi-dimensional feature fusion and multi-scale modeling， the proposed method enhances the representation capability of gait dynamic features effectively， and improves the accuracy and robustness of millimeter-wave radar gait recognition.