To address the problem of stereo matching algorithms having low matching accuracy in non-occluded regions, especially in the weak-textured regions, a non-local stereo matching algorithm based on edge constraint iteration was proposed. Firstly, the proposed method combined the color and gradient information to construct a matching cost computation function. Secondly, the minimum spanning tree structures of left and right images were constructed, and the cost volumes were aggregated according to the smoothness information of the image. Subsequently, a disparity map obtained by the winner-takes-all strategy was used for edge detection. The image edges were then used as constraints to re-aggregate the cost volumes and optimize the results. Finally, dense disparity maps were obtained by the disparity refinement process. The experimental results demonstrate that, for 31 pairs of images from the Middlebury test platform, the average mismatching rate in non-occluded regions of the proposed algorithm is 8.35%. Compared with five existing methods, the proposed algorithm can effectively improve matching accuracy in non-occluded regions.