The visible near-infrared (Vis-NIR) hyperspectral imaging technology was used to identify the intact and damaged Lingwu long jujube rapidly. In this study, damage grades, including Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ of Lingwu long jujubes were obtained by using quantitative damage devices. Hyperspectral images of intact and damaged samples were collected by using a hyperspectral imaging system. Region of interest (ROI) was extracted from the image and average spectral values of samples were calculated. Sample set partitioning based on joint x-y distance (SPXY) was used to divide all samples (420) into calibration sets (315) and prediction sets (105) in a ratio of 3∶1. The partial least squares discriminant analysis (PLS-DA) classification model was established for the original spectrum, and the accuracies of the calibration set and prediction set were 72.70% and 86.67%, respectively. The original spectrum of Lingwu long jujube was preprocessed by means of moving average (MA), Savitzky Golay (SG), multiplicative scatter correction (MSC), orthogonal signal corrections (OSC), baseline and de-trending. PLS-DA classification model was established after pretreatment. The results showed that in the PLS-DA classification model established by spectrum preprocessed by different pretreatment algorithms. Through analysis and comparison, it was found that MSC-PLS-DA was the optimal model combination. In the established classification discrimination model, the accuracies of the calibration set and prediction set were 76.19% and 86.67%, respectively. The accuracy of the calibration set was 3.49% higher than that of the original spectral modeling, and the accuracy of the prediction set was not higher than that of the original spectral modeling. Original spectral and spectral after pretreatment was used to extract feature wavelengths using the successive projections algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and interval variable iterative space shrinkage approach (iVISSA), and established the PLS-DA classification model based on the feature wavelengths. The results showed that MSC-CARS-PLS-DA was the optimal classification model, the accuracy of the calibration set was 77.14%, the accuracy of the prediction set was 89.52%. The modeling accuracy was improved by 4.44% and 2.85% respectively compared with the original spectral modeling accuracy. The above research showed that the Vis-NIR hyperspectral imaging technology combined with MSC-CARS-PLS-DA model could realize the rapid identification of lingwu jujube damage grade.