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Aiming at the problems of image feature point matching algorithm, such as large data volume and long calculation time, a fast feature matching algorithm for improved mesh segmentation statistics is proposed. Firstly, the aspect ratio of the image is taken as the constraint, the image is divided into a plurality of non-overlapping square shape meshes, and the number of rough matching feature points in the grid is counted, and then the modified five-square grid statistical method is used to eliminate the false match. The four adjacent grids of the grid where the feature points are located are taken as the neighborhood range, and the five-square grid feature score is compared with the value calculated by the newly proposed threshold formula, and finally the fine-matched feature point set is obtained; in the OxFord dataset Compared with the actual UAV remote sensing images, the algorithm is compared with various algorithms. The experimental results show that the proposed method can guarantee the accuracy and recall rate close to the current feature point fast matching algorithm. It has increased by 35.6 %, which proves the real-time and effectiveness of feature point matching.