Aiming at the problem of traditional gait recognition algorithm due to the change of clothing and the change of covariate factors such as perspective, this paper proposes a gait recognition algorithm based on improved deep convolutional neural network. The algorithm uses the layered processing mechanism to extract the gait features from the gait data, which can reduce the impact of common changes and occlusion on the recognition accuracy. At the same time, the algorithm determines the optimal number of features of each layer in the network according to experiments. The optimal size of the graph and the type of input features to be used for gait recognition can handle relatively small data sets without any enhancement or finetuning. CASIAB gait database simulation experiments show that CNN proposed in this paper covering the gait recognition problem of cross view gait recognition and no subject, it can overcome the covariate factor problem related to gait recognition, and has better gait recognition accuracy.