Aiming at the problems in content-based image retrieval, such as inconsistence between low-level visual features and the user′s high-level semantics for image understanding, low image retrieval accuracy, and the inability of a single distance measurement method for complete reflection of the similarity degree between images, we propose a remote sensing image retrieval method based on improved fuzzy C-means clustering and convolutional neural network (CNN). This method makes full use of the characteristics of remote sensing images. It adaptively processes the noise of remote sensing images by using Retinex algorithm, and uses CNN to supervise the remote sensing images by multi-layer neural network to extract remote sensing image features. Besides, the modified fuzzy C-means clustering is adopted for feature clustering analysis. Meanwhile, the top-k sorting algorithm which combines the quick sorting algorithm with the distance position weights is applied to improve the retrieval accuracy of the remote sensing images. Experimental results show that this method can significantly improve the performance of remote sensing image retrieval.