As a passive navigation mode, the scene matching navigation of UAVs has been extensively studied. Feature point extracting and matching are important components in scene matching navigation of UAVs. Traditional feature point extracting and matching algorithms do not provide global negative feedback of the results, resulting in low accuracy in heterogeneous image feature matching. To solve the problem, this paper proposes a UAV scene matching navigation method based on deep neural network feature matching. In this method, the deep neural network of SuperPoint and algorithm of LightGlue are introduced and improved for feature point extracting and feature matching, and the accuracy and stability of feature matching are improved. In order to solve the problem of huge difference between pixels in heterogeneous images, an image grayscale conversion algorithm is designed, which effectively reduces the influence of pixel differences on the matching results in the image matching process. Finally, the simulation experiment results show that, compared with traditional ORB algorithm, the deep learning algorithm can solve the feature matching problem of UAVs in complex environments more effectively.