In order to solve the problems of info rmation loss and time consuming in stereo matching of traditional convolutional neural network (CNN),a stereo matching algorithm based on deformable a nd depth separable convolution is designed in this paper.In the process of featu re extraction,deformable convolution and deformable convolution kernel are used to construct residual network to complete adaptive learning and expand the effective receptive field. So as to adapt to different deformations of the object,and to obtain more detai led feature,reduce information loss and improve the matching accuracy.In the process of feature aggregation,the de pth separable convolution is used to construct depth separable aggregation network,the convolution operation is c arried out in the spatial dimension and channel dimension respectively to reduces number of parameters and computati onal complexity,and ensures real-time matching.Tests on related datasets,the experimental result shows th at the network running time of the algorithm is reduced to 1.60s,the three-pixel error rates are 2.84% and 2.79% respectively on KIKKT 2015and KITTI 2012datasets.The endpoints error rate is 1.59% on SceneFlow dataset.Com pared with other benchmark networks,the computational complexity of the network model is reduced and the a ccuracy of the algorithm is greatly improved.