This study proposes an adaptive filtering stereo matching algorithm that fuses edge features to improve the problem of fuzzy matching results at the edge of the images. Moreover, a cost calculation method that fuses multiple features is proposed by introducing the image neighborhood pixel information and image edge features into the Census transform and combining the advantages of the gradient transform algorithm. Then, an edge weight factor is introduced to improve the effect of aggregation. The guided filtering is improved by setting an adaptive edge weight. Finally, the final disparity map is obtained through an optimization process. The proposed algorithm is compared with other algorithms on the Middlebury test set. The results show that the proposed algorithm is very effective in matching the effect at the edge. It also shows that the proposed algorithm has a stronger antinoise performance than other algorithms.