Gait recognition is a type of noncontact remote biometric recognition technology used for identity recognition based on the walking patterns of distant pedestrians. Aiming at the problem of poor recognition effect in infrared human gait recognition using convolutional neural network (CNN), the long and short term memory network (LSTM) is used to cover the image after wearing according to the proportion of human height, so that the network can focus on extracting the change characteristics of legs and the time dimension characteristics of each infrared human gait cycle. Therefore, a new gait recognition model is developed. In the CASIA C infrared gait database provided by the Chinese Academy of Sciences, the experimental test was carried out on the data after the preocclusion processing of the wearing part, and recognition accuracy of the proposed model was higher than that of convolutional neural network model. The experimental results indicated that using LSTM for gait recognition considerably enhanced the recognition accuracy when some features were unavailable.