In order to realize rapid identification of dried red jujubes, this paper proposes a method based on near-infrared hyperspectral imaging technology. The near-infrared hyperspectral images (1 000～1 600 nm) of 240 samples in total from 4 cultivars of dried red jujubes will be acquired. The samples are to be divided into the calibration set and the prediction set in the ratio of 2∶1. 7, 8, 10 effective wavelengths are to be selected by principal component analysis(PCA), x-loading weight(x-LW)and successive projection algorithm(SPA) respectively. The dimensionality of original hyperspectral images will be reduced with PCA, and texture features of the first principal component image are to be extracted with gray-level co-occurrence matrix(GLCM).The partial least squares-discriminant analysis(PLS-DA), back propagation neural network(BPNN)and least square support vector machine(LS-SVM) are to be applied to build identification models with the selected effective wavelengths, texture features and fusion of the former two features. The identification rates of the models based on fusion features will be higher than those of models based on the spectral features or texture features respectively. The BPNN models based on the fusion features will obtain the best results, whose identification rates of prediction set are to be 100%. The results in this paper indicate that the near-infrared hyperspectral imaging technology has great potential to identify the dried red jujubes rapidly.