The hyperspectral super-resolution image set is obtained with the classical super-resolution method and the characteristics of these images are studied. A quality assessment method of hyperspectral super-resolution images is proposed based on three types of image feature vectors. In this method, the spatial natural statistics, the local frequency features and the local binary gradient of images are calculated, respectively, and three kinds of feature vectors are obtained. The regression forest model is established for the three types of low-level feature vectors to predict the image quality scores. Compared with other classical methods, the proposed algorithm possesses high accuracy and good subjective and objective consistency.