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In order to achieve rapid non-destructive identification of apple varieties, the methodology of near-infrared hyperspectral imaging on identification of apple varieties was investigated. Near infrared hyperspectral images with wavelength from 865~1711nm of total 90 sample fruits were collected from three different varieties (“Jonagold”, “Fuji” and “Qinguan” apples), and hyperspectral image area of the apple was selected as a region of interest (ROI). Reflection intensity data of the average reflex spectrum were extracted with resolution rate of 2.8nm, then they were calculated with K-nearest neighbor (KNN) and the support vector machine (SVM) methods, respectively, which were checked with 5-fold cross-validation method. The results showed that the hyperspectral images of three varieties of apples all became clear within wavelength of 941~1602nm. Among ten distance-types’ judgment of KNN with average reflection intensity at 200 wavelength-points, the identification accuracy of Chebychev, Euclidean and Minkowski reached the highest of 100% when the parameter K was set at 3 or 5. While using the support vector machine-radial basis function (SVM-RBF) model, the accuracy rate reached above 92% when the value of γ fell within 2-8～1. The highest recognition rate of this model reached 96.67% when γ was set at 2-5 and C took the value of 16 amd 32 at the same time. The results demonstrated that near-infrared hyperspectral imaging in combination with KNN was excellent and reliable for the rapid identification of apple varieties. This method could provide reference for identifying apple varieties in production.