Remote sensing image matching is one of the fundamental challenges in earth observation. The complexity and diversity of surface information in remote sensing images often pose difficulties for image matching. To overcome these difficulties, a remote sensing image-matching network based on multiscale feature fusion and importance ranking loss is proposed. This network comprises two parts: a key-point detection network and a feature descriptor extraction network. The key-point detection network has a multilayer convolutional structure based on feature pyramids. This structure is designed to achieve multiscale feature fusion at different network levels. Multiple convolution kernels are used to gradually expand receptive fields at the same level, thereby fully capturing multiscale information in remote sensing images. Furthermore, CBAM is used to aggregate the response graph of the key-point detection network to detect key points with significant scores. The key-point detection network is optimized using the score loss and image block loss, and the feature descriptor sub-extraction network is optimized using the descriptor subloss. The score-importance sorting loss function, descriptor sub-importance sorting loss function, and neighbor mask-based descriptor subloss function are specially designed to ensure that the key points, descriptors, and image blocks used for remote sensing image matching have high repeatability and distinguishability, which improves the accuracy of remote sensing image matching. In this study, many remote sensing images were collected and a remote sensing image-matching dataset was constructed via homography transformation. This dataset was used to experimentally verify the performance of the proposed network model. Compared with traditional image-matching methods or other end-to-end deep-learning image-matching methods, the proposed network model has considerable advantages in remote sensing image matching.