Abstract: In order to realize the non-destructive detection of V_C and total sugar contents of jujube at different drying time, a non-destructive detection method based on hyperspectral imaging technology was proposed. In this study, the spectral data collected from jujube with different drying time were processed with spectral pretreatment and feature wavelengths screening (successive projections algorithm) to obtain the feature wavelengths, the texture data of the grayscale images at the feature wavelengths were extracted by gray level co-occurrence matrix (GLCM). The partial least squares (PLS) model was built based on feature wavelengths, image feature, and fusion features. As for the prediction models of V_C content and total sugar content, the results showed that no pre-processing and Autoscale pre-processing were the best methods of pre-processing, and the PLS prediction models based on feature wavelengths and data fusion had the best prediction performance with coefficient of determination for prediction (R_P^2 ) of 0.930 and 0.883, and the mean square error for prediction (RMSEP) of 30.439 mg/100 g and 0.0400 g/g, respectively. The residual predictive deviation of the model was over 2.5, indicating a good prediction performance. The results showed that hyperspectral imaging technology could be used for non-destructive detection of V_C and total sugar contents during the drying process of jujube.