Image matching, which refers to transforming the image to be matched into the coordinate system of the original image, plays important roles in numerous visual tasks. The feature-based image matching method, which can find distinctive features in the image, is widely accepted because of its applicability, robustness, and high accuracy. For improving the performance of feature matching, it is important to obtain more feature matches with high matching accuracy. Aiming at the sparse matching problem of the traditional feature matching algorithm, we propose a dense feature matching method based on the improved deep feature matching algorithm. First, a series of feature maps of the image are extracted through the VGG neural network, and nearest-neighbor matching is performed on the initial feature map to calculate the homography matrix and perform perspective transformation. Then, deep features are fused according to the frequency-domain matching characteristics of feature maps for coarse feature matching. Finally, fine feature matching is performed on the shallow feature map to correct the results of coarse feature matching. Experimental results indicate that the proposed algorithm is superior to other methods, as it obtains a larger number of matches with a higher matching accuracy.