Rock microscopic image stitching is a key part of rock analysis and research. The rock microscopic images are large in number (hundreds of images) and rich in content and contain many similar and confusing areas, which result in low stitching efficiency and low alignment accuracy. In addition, the stitching of multiple images will result in error accumulation and misalignment. For this problem, a similar region-SURF (SR-SURF) method for rock microscopic image stitching is proposed. Firstly, similar regions are quickly extracted by using hash fingerprints. Secondly, feature points are detected in this region. Then the improved random sample consensus (RANSAC) algorithm is used to eliminate the wrong matching points. The misaligned image is corrected by the best matching template. Finally, the least squares method is introduced to eliminate the cumulative error caused by the cumulative multiplication of homography matrices. The experimental results show that the algorithm proposed in this study eliminates the cumulative error caused by multiple image stitching and solves the problem of stitching misalignment, which improves the stitching speed and alignment accuracy. It has high practical value and promotes the digital storage process of rock slices.