Most of the existing gait recognition methods are based on silhouettes or skeletons, however, the 2D information lacks a complete description of the spatial geometry of the human body, and the performance of the recognition effect is limited under complex conditions such as view angle change and occlusion, for this reason, this paper proposes a point cloud gait recognition method that combines global multiscale and local fine-grained features. The method projects the point cloud as a depth gait map, introduces a cross-view data transformation module to improve the viewpoint invariance of the model, uses an improved residual network to extract rich global multi-scale gait features, and finally uses a KAN network to enhance the representativeness of local fine-grained gait features. The experimental results show that the gait recognition method based on point cloud is far better than the method based on 2D information, which achieves an average Rank1 accuracy of 92.65% on the SUSTech1K public dataset, which is a 6.02% improvement compared to the advanced method LidarGait, which fully verifies the effectiveness of the method.