To address the problem of sharp decrease in recognition accuracy caused by covariate factors such as camera view or pedestrian occlusion in gait recognition, an improved feature enhancement GaitPart cross-view gait recognition method IFE-GaitPart (An Improved Feature Enhancement GaitPart) is proposed, which improves the network model into The method improves the network model into a two-path parallel form containing a spatial feature extraction branch and a significant temporal modeling branch, and first uses a convolutional network to extract shallow features from the original input sequence as the input of the two-path network, then proposes a non-uniform convolutional approach to extract fine-grained information of gait in spatial feature extraction and fuses global features to improve the information capacity of spatial features, while in the significant temporal modeling branch, proposes An adaptive multi-scale temporal feature extraction module is proposed on the significant time modeling branch to obtain the short-term dependence and global time cues of the gait in the time dimension, and the complete temporal information is obtained after stitching. Finally, the temporal features are stitched in the channel dimension and the gait features are output using a fully connected layer. The experimental results show that the effectiveness of the method is demonstrated by achieving 97.6%, 94.5% and 81.1% Rank-1 accuracy on the CASIA-B dataset with normal walking, carrying luggage and wearing outerwear, respectively.