Hyperspectral imaging technology was employed to detect defects such as rot, bruise and rust in Malus asiatica Nakai. 213 RGB images of samples, including 3 types of damage samples and sound ones, were acquired by hyperspectral imaging system. Spectral data were extracted from the regions of interest (ROI) using ENVI4.7 software. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to select characteristic wavelength points. As a result, 11 and 6 characteristic wavelength points were chosen for CARS and SPA respectively. Extreme learning machine (ELM) discrimination model was established based on the spectral data of selected wavebands. The results showed that the accuracy of the SPA-ELM discrimination model was as great as 94.74%. Then, images corresponding to six sensitive bands (532 nm, 563 nm, 611 nm, 676 nm, 812 nm, and 925 nm) selected by SPA were selected for principal components analysis (PCA). Finally, the images of PCA were employed to identify the location and area of a defect’s feature through imaging processing. Through sobel operator and region growing algorithm, the edge and defective feature of 38 Malus asiatica Nakai can be recognized and the detection precision was 92.11%. This study demonstrated that the defects, (rot, bruise, and rust) of Malus asiatica Nakai can be detected in spectral analysis and feature detection in hyperspectral imaging technology, which provides a theoretical reference for the online detection of defects in Malus asiatica Nakai.