Nondestructive detection of external and internal quality parameters of jujube is crucial for improving jujube's shelf life and industry production. Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to acquire both spatial and spectral information from a sample. It takes the advantages of the conventional RGB, near-infrared spectroscopy, and multi-spectral imaging. In this work, hyperspectral imaging technology covered the range of 450~1000 nm has been evaluated for nondestructive determination of "natural defects" (shrink, crack, insect damage and peck injury) and soluble solids content (SSC) in Huping jujube fruit. 400 RGB images were acquired through four different defect (50 for each stage) and normal (200) classes of the Huping jujube samples. After acquiring hyperspectral images of Huping jujube fruits, the spectral data were extracted from region of interests (ROIs). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (280) and test set (120) according to the proportion of 3:1. Seven principal components (PCs) were selected based on principal component analysis (PCA), and seven textural feature variables (contrast, correlation, energy, homogeneity, variance, mean and entropy) were extracted by gray level co-occurrence matrix (GLCM). The least squares support vector machine (LS-SVM) models were built based on the PCs spectral, textural, combined PCs and textural features, respectively. The satisfactory results show the correct discrimination rate of 92.5% for the prediction samples, as well as correlation coefficient (Rp) of 0.944 for the prediction set to calculate SSC content based on PCs and textural features. The study demonstrated that hyperspectral image technique can be a reliable tool to simultaneous detection of external ("natural defects") and internal (SSC) quality parameters of Huping jujube fruits, which provided a theoretical reference for nondestructive detection of jujube fruit.