A self-supervised stereo matching algorithm is proposed based on common view. In this algorithm, the common visible region of the binocular images is determined according to the left-right consistency of disparity and thus the noise generated in the occluded region is suppressed, which provides more accurate feedback signals for the network model learning. The research results show that the prediction error of the proposed algorithm can be reduced by 11%-42% without any label data, and the performance of the proposed algorithm is comparable to that of the supervised stereo matching algorithm.