With the continuous progress of deep learning theory, end-to-end stereo matching network has achieved remarkable results in the fields of automatic driving and depth sensing. However, the most advanced stereo matching algorithm still have trouble in accurately recover the edge contour information of the object. In order to improve the accuracy of disparity prediction, in this study, we propose a stereo matching algorithm based on edge detection and attention mechanism. The algorithm learns parallax information from stereo image pairs and supports end-to-end multi task prediction of parallax map and edge map. In order to make full use of the edge information learned by the two-dimensional feature extraction network, we propose a new edge detection branch and multi feature fusion matching cost volume. The results show that the edge detection scheme based on the model helps to improve the accuracy of parallax estimation. The error matching rate of the obtained parallax map on KITTI 2015 test platform is 1.75%. Compared with pyramid stereo matching network, the accuracy of parallax map is improved by 12% and the running time is reduced by 20%.