Aiming at the problem that gait recognition is vulnerable to environmental interference, this paper focuses on the gait feature extraction method, an improved attitude estimation algorithm is proposed based on the anti learning network framework to extract gait features. The improved residual network is used to obtain the gait features from low level to high level. With the deepening of the number of network layers, the residual network is adjusted accordingly to highlight the focus on the local detail feature information; A timing encoder is designed, which not only improves the generalization of gait features to environmental changes, but also reduces the impact of environment on feature extraction. Finally, a large number of experiments are carried out based on CASIA data set under three different experimental modes, the recognition accuracy is more than 83%, which finally proves that the feature extraction method proposed in this paper shows good flexibility in complex environment.