The information contained in gait frames is different, and the contribution of different frames to recognition tasks is also different. However, each frame has the same degree of attention in the input layer, this prevents the network from focusing on keyframes. Therefore, we propose a keyframe extraction module via information weighting, make network can pay more attention to the high contribution frame at the input layer, and the extraction of the distinctive features is improved. Moreover, the range of motion in different parts of the human body is different, the temporal and spatial correlation of local feature between silhouettes is different. Based on the discovery, we propose a Local Features Flow Regulation module to calculate the correlation coefficient of the local features of each silhouette, and the regulation coefficient is generated by the correlation coefficient. The regulation coefficient is applied to regulate the flow of local features, this enables the network to capture areas with more spatial and temporal features. Through the extraction of frame-level features and the interaction of local features between frames, the network can extract the most discriminative features from global to local flexibly. During training, each horizontal part is trained separately. The training can adjust the regulation coefficients, and the network is more flexible and expressive. Our model has a good performance on cross-viewing and complex environments of CASIA-B dataset. In the case of normal environment and complex environment (pedestrian with backpacks and in coats), the rank-1 of the proposed model is 95.1%, 87.9% and 74.0% respectively, higher than state-of-the-art.