In this study, the hyperspectral imaging technology is employed for accurately and efficiently detecting the surface damage of Korla fragrant pears. Eighty fragrant pears were considered in this study. The hyperspectral images of the intact and damaged samples in the wavelength range of 400-1000 nm were obtained. The hyperspectral image obtained at 863 nm was selected to achieve image mask using the statistical analysis method. The dimension of hyperspectral data was reduced via principle component analysis. Subsequently, the second principle component image exhibiting the most considerable difference between the damaged and background areas was selected to compare with the fourth principle component image via the ratio method of image processing for enhancing the difference between the damaged area and the background area. Finally, the threshold segmentation and morphological operations were used to obtain the damaged areas on the surface of fragrant pears. Results denote that the proposed method can effectively identify the surface damage of fragrant pears. Furthermore, the accuracy, precision, and recall rate of the proposed method are 93.75%, 87.50%, and 100%, respectively.