The high mismatching rate of the parallax discontinuity region and the repeated texture region has been a major issue affecting the measurement accuracy of binocular stereo matching. For these reasons, we propose a stereo matching algorithm based on multi-feature fusion. Firstly, the weight of neighboring pixels is given using Gaussian weighting method, which optimizes the calculation accuracy of the Sum of Absolute Differences (SAD) algorithm. Based on the Census transformation, the binary chain code technique has been enhanced to fuse the average gray value of neighborhood pixels with the average gray value of gradient image, and then the judgment basis of the left and right image corresponding points is established, and the coding length is optimized. Secondly, an aggregation technique has been developed that combines the cross method and the improved guide filter to redistribute disparity values with the aim of minimizing false matching rate. Finally, the initial disparity is obtained by the Winner Take All (WTA) algorithm, and the final disparity results are obtained by the left-right consistency detection method, sub-pixel method, and then a stereo matching algorithm based on the multi-feature SAD Census transform is established. The experimental results show that in the testing of the Middlebury dataset, the average mismatch rates of the proposed algorithm for non-occluded regions and all regions are 2.67% and 5.69%, the average error of the 200-900 mm distance is less than 2%, and the maximum error of the actual 3D data measurement is 1.5%. Experimental results verify the effectiveness of the proposed algorithm.