Gait recognition algorithms mainly rely on the contour sequence of pedestrian targets for feature extraction and recognition. In practical applications， pedestrians walk together， and the contour is easily occluded and interfered by other pedestrians， which significantly reduces the accuracy of gait recognition algorithm. To improve the robustness of gait recognition algorithm in dense occlusion scene， a deep-learning gait recognition algorithm based on unordered contour sequences is proposed. First， a simulation is conducted based on the Casia-B dataset， and the target contour simulation dataset for dense occlusion scene is established to verify the occlusion robustness of the algorithm. Second， a data augmentation method based on random binary expansion is proposed. However， owing to the limitations of horizontal pyramid pooling （HPP） structure in the area of gait recognition demonstrated through theory and experiment， a degenerated horizontal pyramid pooling （DHPP） structure is proposed. By combining the DHPP structure， CoordConv method， joint training， and pruning method， the perception ability of absolute position information in deep-learning features can be enhanced and the robustness of the algorithm for occlusion scene can be improved. In addition， the feature expression dimension of the target is reduced. The experimental results indicate that the proposed method is effective in improving the robustness of gait recognition algorithm.