In view of the current many image matching algorithms that mainly rely on threshold adjustment to complete feature matching, resulting in low matching accuracy and poor robustness of the algorithm. In this paper, an image matching method using ratio consistency constraints is designed on the basis of matrix. The box filter is used to approximate the Gauss partial derivative function, and the determinant is obtained by convolution of the box filter and the image on the basis of the matrix. The image features are extracted by the determinant. Hu invariant moments are computed in the neighborhood of feature points and their eigenvectors are obtained. Based on the vectors of feature points, the region energy of the neighborhood of feature points is introduced to obtain the matching results. Using the distance relationship between matching points in space, the ratio consistency constraint method is established. The Euclidean metric ratio of matching points is used to search for wrong matching and optimize the result of feature matching. Through experimental analysis, it is found that the matching results of the proposed method have better matching accuracy and robustness than those of the current methods.