Near-infrared spectroscopy (NIRS) combined with least squares support vector machines (LS-SVM) was used to establish a new method for the determination of the hesperidin content in guogongjiu medicinal wine. Firstly, training set was partitioned by Kernard-Stone (KS) algorithm. Secondly, spectral pretreatment methods were discussed in detail, comparing smoothing, rangescaling, autoscaling, first derivative, second derivative, along with those methods combined. Smoothing, first derivative and rangescaling were used for the pretreatment of the NIR spectra of guogongjiu medicinal wine. Thirdly, the effective interval was selected for 8 211-8 312 and 9 712-9 808 cm-1 by synergy interval partial least squares (siPLS). Finally, the model was established by LS-SVM, the root mean square error of cross validation (RMSECV) is 0.001, root mean square error of prediction (RMSEP) is 0.004, and relative deviation of predicting set is less than 5%. It was compared with siPLS, radial basis function neural network (RBF-NN), and SVM, The result shows that the method is rapid, non-destructive, and credible. It is an effective measurement for determining the hesperidin content in guogongjiu medicinal wine.