This paper presents an improved stereo matching algorithm based on PSMNet. In the feature extraction stage, the new SPP feature pyramid module can better aggregate the environmental information of different scales and different locations to construct cost volume, in order to make full use of the global environmental information. When constructing the matching cost volume, the group correlation strategy is proposed to make full use of the global and local information in features. In the cost aggregation stage, the hourglass structure is optimized and the channel attention mechanism is introduced so that the network can extract the information features with high representation ability and high quality channel attention vector. In order to further optimize the disparity map, a disparity optimization network is designed to improve the initial disparity estimation. The method in this paper is evaluated on Scene Flow, KITTI 2012 and KITTI 2015 stereo datasets, and the average prediction error EPE of the proposed model on Scene Flow dataset is reduced to 0.71 pixels. The mismatching rates on KITTI 2012 and KITTI 2015 stereo datasets decreased to 1.20% and 1.86%, respectively. The experimental results show that the proposed method achieves superior performance.