Aiming at the noise-sensibility and low matching rate of existing local matching algorithms, a stereo matching algorithm based on Spearman correlation coefficient and multi-scale framework was proposed. Two cost calculation models were proposed by using a fixed window and by simplifying the Spearman correlation coefficient. Then, a multi-scale framework was fused to perform cost aggregation on the image pyramid in order that the matching algorithm could obtain a higher matching rate in the low-texture region. The experimental results show that the proposed stereo matching algorithm effectively reduces the false matching rate: the average mismatching rate of 31 standard image pairs in the Middlebury 2.0 test set is only 7.89%, and the average of 15 standard image pairs in Middlebury 3.0 is 13.45%. Therefore, the method can effectively reduce the mismatching rate of images and has better robustness against noise.