Gait recognition refers to the technology of identity verification by identifying the walking posture of pedestrians. Different from the physiological characteristics such as fingerprints and palmprint that need close contact, gait, as a behavioral feature, has the characteristics of high non-invasive, low camouflage and long-distance recognition. Therefore, gait recognition has broad application prospects in various fields. This paper proposes a gait feature recognition method based on capsule network, and introduces spatial attention mechanism in the capsule network to improve the weight of effective gait features in the capsule, and updates the input image through the design of feedback weight matrix to improve the performance. The designed gait recognition model based on capsule network has been tested on casia-b dataset. The average recognition rate is 93%, 85% and 67% respectively under three different walking conditions: normal walking, walking with bags and walking with coats. At the same time, a multi view gait recognition experiment is carried out on the ou-mvlp data set, and the average recognition rate reaches 85%.