In order to verify the relationship between spectrum and lettuce water loss, chlorophyll and freshness, this research employed Vis-NIR (280~1100nm) hyperspectral imaging system to collect the spectral data of lettuce under different storage conditions with storage time, and measured the water loss change and chlorophyll content at the same time. The original spectra were preprocessed by standard normalized variate (SNV), multicative scatter correction (MSC), Savitzky-Golay (SG), which were further analysised by the first-order differential and second-order differential methods and then the characteristic wavelength was selected by competitive adaptive reweighted sampling (CARS) algorithm and successive projection algorithm (SPA). The prediction models were established with the original spectrum and effective wavelength respectively by least squares support vector machine (LS-SVM) and BP neural network (BPNN) to predict the freshness and water loss rate of lettuce. The results show that BPNN has a satisfactory predicting accuracy, and the prediction accuracy of water loss rate reaches 82.5% and the prediction accuracy of freshness is as high as 95%. Finally, spectral changes at different storage times were analyzed by principal component analysis (PCA), and extracted the principal component images to visualize the wilting process, then, it demonstrated fresh lettuce under soaking and refrigerated storage conditions showed a significant delay in the wilting process compared with room temperature. In addition, the chlorophyll content could not be used to correctly describe lettuce freshness because it showed a trend of increasing and then decreasing in different time periods. It can be seen that hyperspectral imaging technology can realize the effective determination of lettuce freshness and water loss.