This study investigated the feasibility of hyper-spectral imaging techniques to estimate the vigor of heat-damaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370-1042 nm hyperspectral imager that included vis-ible and near infrared light,germination was tested to con-firm estimates.The Savitzky-Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),prin-cipal component analysis,and local linear embedding algo-rithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabilis based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81％.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.