Gait recognition has the advantages of requiring no contact, non-invasion and easy detection. However, for the cross-view gait recognition, the contour of pedestrians varies with the change of people's viewpoints, thus affecting the performance of gait recognition. Therefore, a capsule network with sharing transformation matrix and its improved dynamic routing algorithm were proposed, which reduce the network training parameters. On this basis, by fusing the view features and using Triplet loss and Margin loss, a cross-view gait recognition model fused with view features was proposed. Experimental result on CASIA-B dataset show that it is feasible to extract gait features using the capsule network with sharing transformation matrix. Under the conditions of normal walking, carrying a bag, and wearing a coat, the proposed model fusing view features has the recognition accuracy improved by 4.13% compared to the cross-view gait recognition method based on convolutional neural network, and has better performance for gait recognition across large views.