To identify different Xinjiang jujube varieties, a hyperspectral technique and machine learning algorithms were employed to obtain and analyze the spectral data of Jinsi-jujube, Jun-jujube, and Tan-jujube. First, the original spectra were preprocessed using various data preprocessing methods, including multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first-derivative (1-Der), and Savitzky-Golay (SG) smoothing. The effects of the preprocessing methods on modeling were investigated. Then, the samples were divided into calibration and prediction sets using sample set partitioning methods based on joint X-Y distance (SPXY). The jujube variety identification models were established based on linear discriminant analysis (LDA), K-nearest neighbor (KNN), and support vector machine (SVM) algorithms using the preprocessed full-band spectra. The results demonstrate that 1-Der outperformed other preprocessing methods mentioned above. Next, the characteristic bands were extracted from the full-band spectra using principal component analysis (PCA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS). Then, the jujube variety identification models were established based on the characteristic bands. The CARS-based models achieved the highest accuracy in the models established based on several characteristic band extraction methods. Finally, taking the SVM model as an example, the model runtime was compared. The time required by the SVM model based on the characteristic bands was much shorter than the time required by the model based on the full-band spectra.