Abstract: Aimed at the weak learning and classification ability of the backbone network in the GaitSet algorithm, the gait recognition algorithm based on the multi-feature fusion convolution (MFFC-GaitSet) is proposed. The algorithm reconstructed the GaitSet network by multi-feature fusion convolution to enhance the network learning ability, and smoothed and optimized the ternary loss function. The gait contour map was repaired by morphological processing. The algorithm was validated on the Casia-B dataset and achieved a gait recognition accuracy of 85.811%, with the increase of 2.6%. The model weight was increased by only 6%. The algorithm could effectively reduce the negative influence of complex environment on gait recognition and achieve high-precision gait recognition in complex environment. The experimental results show that the method can achieve more accurate gait recognition with better robustness and generalization ability.