In gait recognition, the discriminative gait feature cannot be extracted due to the occlusion of clothing and backpack, which leads to the low recognition accuracy. A gait recognition method combining ResNet and multi-level block structure is proposed in this paper. First of all, the gait energy map is divided into different scales in the horizontal direction to extract the fine-grained features of different regions, which reduce the impact of local occlusion on other regions. At the same time, in order to better learn the characteristics of the region with the highest motion frequency, the Inception module is added. Secondly, in order to improve the recognition accuracy of the network model, cross-entropy loss, triple loss and L2 regularization are utilized to constrain the weight of the residual network. Finally, experiments were processed in the public gait data set CASIA-B and OU-ISIR Treadmill B, and the recognition rate reached 87. 5% and 82. 6% under different clothing or backpack conditions. It is indicated that under these conditions, the method could obtain favorable veracity and good robustness.