【Objective】By studying the hyperspectral characteristics of apple damage and establishing the best classification model of apple damaged area based on hyperspectral imaging, we can provide an important basis for realtime, rapid and accurate identification of apple damage.【Method】This paper investigates the use of hyperspectral image technology to detect mechanical damage on fruit surfaces using apple samples collected from Pinggu District, Beijing as the research object. Using the hyperspectral image (HSI) data in the range of 390-1 000 nm, the spectral response characteristics of the damaged and normal ROI areas was analysed by ratio spectroscopy, and three types of spectral indexes, namely the normalized difference spectral index (NDSI), the ratio spectral index (RSI) and the difference spectral index (DSI) were selected and constructed to better highlight the characteristics of the damaged areas..On the basis of these indexes, the spectral indexes with better ability to extract damage areas were selected, and the unsupervised data clustering algorithm of iterative self-organizing data analysis techniques algorithm (ISODATA) was used to extract apple damage areas.【Result】The spectral reflectance changed significantly when the apple surface was damaged. After waveband optimization, it was found that the reflectance of 528 nm and 676 nm could effectively identify the abnormal areas. Based on the selected feature bands, the recognition spectral indexes for apple damage detection were constructed, including NDSI, RSI and DSI. By the pixel value analysis of the spectral index images, the damaged area features were distinguished from the normal area features in each SI enhanced image obviously. Among them, the difference between the NDSI pixel averages of the two types of image features was the largest, reaching 0.629, which indicated that the established NDSI has a strong ability to distinguish between the damaged area and normal area features. Using the unsupervised classification method ISODATA classification, it was verified that the spectral feature index has high specificity in detecting apple damage, and the accuracy rate of apple damage reached 92.50%.【Conclusion】The results are applicable to the real-time and rapid detection of apple damage, providing a technical basis and reliable reference for the accurate management of apple production.