Aiming at the issue of accurate recognition of human motion gait in exoskeleton robot human computer interaction and medical rehabilitation， a dynamic gait recognition method based on human model constraints was proposed. Firstly， Anybody Modeling System （AMS） simulation software was used to establish different motion simulation models， the gait phases were devided according to the model constraints， and the corresponding relationship between the real data and the simulation data was established through regression mapping. Then， the plantar pressure data collected by the flexible pressure sensor and the foot displacement data collected by the inertial measurement unit were fused into the foot motion data， and the motion data was dynamically segmented according to its dynamic changes and the model constraints to determine the gait phase. Finally， Convolutional Neural Network （CNN） was built to identify the walking gait phase. Experimental results show that the proposed method has the average recognition accuracy of walking action gait of 94.58%， and the average gait recognition accuracy for going upstairs and downstairs actions is 93.21% and 94.64% respectively， which has the gait recognition accuracy of the three actions （walking， going upstairs and downstairs） increased by 11.34， 12.19 and 16.03 percentage points， respectively. It can be seen that CNN recognition based on dynamically segmented foot motion data has a high accuracy， and is suitable for gait recognition of different actions.