Significant progress has been made in no-reference image quality assessment in terms of color harmony in recent years. However, most methods do not consider color contrast and lose some color information in the quantification process, so they perform poorly when evaluating the color harmony of color images. An image quality assessment model based on color descriptors is established under the inspiration of natural scene statistics. The model introduces local color contrast features of color fusion images and combines them with visual attention mechanism. The local color statistics coefficients of images in both RGB color space and CIELAB color space are calculated to reduce the loss of color information. Furthermore, a multi-scale content-aware network is used to extract multi-scale content features and establish the connection between color features and image content. Finally, the image quality score is obtained by the quality assessment network. The dataset validation and experimental results reveal that the proposed model, compared with the existing 15 methods, has good agreement with human visual perception and better performance.