Objective Aiming to solve the problem of difficulty in quickly obtaining dense disparity map with high precision, a stereo matching algorithm was proposed based on edge features and confidence. Methods In order to increase the distinction between the pixels, the algorithm used AD-Census function as the matching cost measure function. Targeting at the mismatch problem caused by the matching window across the parallax discontinuity region, firstly we obtained sparse feature points of edge from the reference image, and then obtained the matching window with self-adaptive size and shape based on edge feature constraints. Using the WTA algorithm to calculate the disparity of each pixel when computing parallax, and meanwhile calculate the confidence of each pixel’disparity. Finally, we repaired the disparity of pixels with low disparity confidence through combined optimization of edge detection image and confidence image. Results The experimental results showed that the algorithm could fast and effectively deal with the mismatch problems of occlusion and disparity discontinuities. Conclusion The stereo matching algorithm based on edge features and confidence is an efficient and feasible stereo matching algorithm.