The varieties of cabbage seeds directly affect the yield and quality of cabbage, in order to rapidly and nondestructively identify the varieties of cabbage seeds, near infrared spectra technique were applied in this study and reflectance spectrum of the cabbage seeds was obtained. Firstly, to excavate the effective information in the spectral data and improve signal to noise ratio, the raw spectra was pre-processed with the method of standard normal variate (SNV) and multiplicative scatter correction (MSC). Secondly, principal component analysis (PCA) was used to analyze the clustering of cabbage samples, then the characteristic differentia of three cabbage varieties was obtained through qualitative analysis. Six Effective wavelengths were selected by successive projections algorithm (SPA). Finally, the full spectra variable, the first three principal components (PCs) using PCA and selected effective wavelengths using SPA were respectively set as inputs of the partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models for the classification of cabbage seeds. As can be seen from the two dimensional plot drawn with the scores of PC1 and PC2 (the first two principle components), PC1 and PC2 had a good clustering effect for different kinds of cabbage seeds. LS-SVM models performed better than PLS-DA models, the correct rates of discrimination were 100% achieved with LS-SVM models. PLS-DA and LS-SVM models built based on the selected wavelengths performed better than the models built based on the first three principal components, moreover, the SPA-LS-SVM model obtained the best results among all models, with 100% discrimination accuracy for both the calibration set and the prediction set. The overall results show that SPA can extract wavelengths, and the LS-SVM model combined with SPA can obtain optimal classification results. So the present paper could offer an alternate approach for the rapid discrimination of cabbage seeds variety.