Abstract: Feature matching is one of the key steps to restore a 3D model from an image. To effectively improve the quality of 3D reconstruction, an enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction is presented. Firstly, based on the grid-based motion statistics algorithm, an enhanced motion consistency concept is proposed by adding a threshold β, which enhances the judgment condition of true and false matching points, avoids the false matching of highly similar features, and improves the initial matching points accuracy. Then, the RANSAC algorithm is used for feature point matching optimization to filter out outliers and further improve the feature point matching accuracy. Finally, a guided diffusion concept that combines guided matching and motion consistency is proposed, which reduces the possibility of concentrated distribution in the part of the image, thereby improving the feature points matching number and the 3D model stability. Experiments on 618 pairs of images in the public 3D reconstruction datasets demonstrate that this algorithm can achieve better performance in feature matching and 3D reconstruction. For the success percentage of pose estimation less than error threshold, the proposed algorithm is 22.58% and 12.90% higher than the SIFT-based ratio test algorithm and the GMS algorithm, respectively. In particular, it is 46.15% and 30.77% higher on repeated texture image pairs.