The scale-invariant feature transform (SIFT) algorithm is widely used in image matching. To reduce its computational complexity, a fast SIFT image matching algorithm based on mask search is proposed. First, the texture information of the image is analyzed, and the corner response function (CRF) of the Harris algorithm is used to divide the image. The regions with higher texture complexity are used as a Mask to generate a Mask pyramid ,therefore reducing the search space of the feature points; then, a seven-zone circular descriptor is established in the polar coordinate system and its dimension is reduced. Finally, the same kind matching is carried out according to the extreme value category of feature points to reduce the matching complexity. Experimental results show that the method using Mask feature searching can improve the overall speed of the algorithm at the cost of low matching quality, and it can further improve the speed of the algorithm combined with the improved descriptor and extreme value classification algorithm. Therefore, the proposed algorithm has the potential value in the application of high matching efficiency requirements.