To solve the problem of low matching accuracy for weak-texture regions in stereo matching of glass-ceramics, this paper proposes a stereo matching algorithm based on scale invariant feature transform (SIFT) feature matching combined with region matching based on local entropy. First, the local entropy map of the image is obtained by introducing Gaussian weighting and gradient amplitude into the local entropy calculation, and the high entropy region is extracted. Then, the improved SIFT algorithm is used to detect the feature points in the high entropy region, and the feature matching results are obtained. Finally, the improved normalized cross correlation (NCC) region matching is used to fuse the results to obtain dense disparity results. Experimental results on datasets show that, the average matching rate of the proposed algorithm is 91.45%, which is 6.89 percentage points, 5.68 percentage points, and 5.75 percentage points higher than that of the semi-global matching algorithm, AD-Census algorithm, and PatchMatch algorithm, respectively. Results indicate that the proposed algorithm can deal with the problem of low matching accuracy in weak-texture regions and improve the matching accuracy.