In cross-view gait recognition, it is difficult to extract distinguishable and diverse gait features in the case of clothing occlusion, which leads to the decrease of recognition accuracy. A multi-scale feature fusion network based cross-view gait recognition method is proposed. This method can effectively utilize the complementarity among gait features to obtain gait features with discriminability and diversity, thereby solving the problem of poor discriminability and uniformity caused by clothing occlusion, and thus improving the accuracy of cross-viewing Angle gait recognition. In order to verify the effectiveness of the proposed method, the public data set CASIA-B was used to verify the proposed method. The experimental results show that the proposed method achieves 73.4% recognition performance for the cross-viewing Angle gait recognition problem with occlusion, and 95.5% and 88.0% recognition performance under normal and backpack walking conditions, respectively. In addition, the performance of our method is better than that of other typical gait recognition methods under occluded conditions.