An image matching algorithm is proposed to address the poor robustness and low matching efficiency exhibited by the affine scale-invariant feature transform algorithm used for image matching with a large viewing angle. The proposed algorithm employs nonlinear diffusion filtering to preprocess images instead of Gaussian linear filtering, thereby improving the robustness of the detected feature points. Further, a mask operator is employed to denote the effective region in the image simulation transformation process for improving the detection efficiency of feature points. According to the angle simulation transformation principle, the neighborhood information can be extracted from the feature points at different view transformation angles, and the multi-view nearest neighbor matching and weighted matching rules are proposed to establish a multi-view descriptor, thereby improving the matching efficiency. The experimental results show that the proposed algorithm not only exhibits good robustness when the viewing angle changes, but also improves the image matching efficiency and accuracy compared with the existing feature matching algorithms.