A quality assessment method of remote sensing images is proposed based on deep learning and the human visual characteristics. The convolutional neural network and the back propagation neural network classifiers are used for the simultaneous feature learning and grade classification of blur and noise intensity for the remote sensing images. The masking effect and the corrected assessment model of the perceptual weighting factors are used to obtain the quality assessment results of remote sensing images, which are more in line with the human visual characteristics. The research results show that the proposed method can effectively solve the difficulty in the quality assessment of remote sensing images with both blur and noise. Moreover, the quality of remote sensing images can be effectively and accurately evaluated, and the results are well in good agreement with both the subjective evaluation results and the human visual experiences.