<p>Accurate classification of snow pear damage enables optimized utilization, enhancing economic value. Multispectral image (MSI) has the advantages of efficient, non-destructive, and accurate detection. Thus, the study was based on MSI to achieve accurate detection and degree classification of snow pear damage. The quantitative damage device was used to create the sample damage of snow pear. The MSIs of snow pear were captured by the near-infrared industrial camera equipped with 5 optical band-pass filters. Then, the Euclidean distance between their average spectral values was calculated. The damage degree of snow pear was classified comprehensively according to the relative position of Euclidean distance in the coordinate system, the clustering analysis result of the K-means clustering algorithm (K-means). The collected MSIs of snow pears are processed by binarization, morphology, and contour extraction in turn. Besides, the datasets were categorized by selecting the average spectral values of snow pear’s region of interest (ROI) under different numbers of impacts. Support vector machine (SVM) model, random forest (RF) model, and adaptive boosting (AdaBoost) model were built to classify them respectively. The performance of the above-built model was evaluated based on the test set, where the performance of the AdaBoost model was higher, with a detection accuracy of 93.16%. The research results show that MSI technology can accurately, effectively, and quickly identify the slightly damaged snow pears, and classify the snow pears with different degrees of damage, which provides a new method for the practical application of snow pear damage detection and damage degree classification.</p>