As a hot topic in computer vision, binocular stereo matching has broad applications in various tasks such as distance perception, remote sensing, and autonomous driving. An end-to-end disparity estimation method based on an improved attention concatenation cost volume network is proposed herein to address the challenges of depth discontinuity and inaccurate disparity prediction in boundary regions observed in current methods. First, a multiscale feature fusion network is introduced to combine multiscale feature maps containing rich information from both shallow and deep layers. This approach enhances the fine-grained representation of image details and mitigates the problem of inaccurate disparity prediction in areas with depth discontinuities. Subsequently, a Sobel edge smoothing loss is designed to establish a constraint between the disparity map boundary and the scene's edge contours, alleviating inaccuracies in disparity prediction at the image's target boundaries. Experimental verification of the proposed method on the Sceneflow dataset reveals that the proposed method achieves 0.467 score in the EPE metric and 1.51% in the D1 metric. On the KITTI dataset, the method achieves 1.44% score in the 3-All metric and 1.61% in the D1-All metric. Compared to the attention concatenation cost volume network, the proposed method shows reduced EPE and D1 scores by 3.51% and 5.63%, respectively, and reduced 3-All and D1-All metrics by 2.04% and 2.42%, respectively, demonstrating superior disparity estimation performance.