Abstract: Aiming at the problems of huge parameters and inaccurate accuracy in stereo matching algorithm based on convolution neural network(CNN),this paper proposes an efficient and accurate stereo matching algorithm based on CNN.Firstly,a feature extraction network combining multi-dimensional context information is designed to improve the matching accuracy of ill-posed regions.Secondly,the existing similarity calculation steps are improved to reduce the amount of the network parameter while ensuring the matching accuracy.Finally,a lightweight attention-based disparity refinement algorithm is proposed,which focuses on and modifies the erroneous pixels of the initial disparity map from the channel and spatial dimensions.Compared with GC-Net on the standard dataset Sceneflow,the proposed algorithm improves the matching accuracy by more than 50%while reducing 14%parameters.