Ear rot of corn seeds is one of the main diseases that harm the yield of corn. A discriminant model of ear rot of corn seeds was studied by near-infrared spectroscopy. The study samples were provided by the Hainan Breeding Base of Jilin Academy of Agricultural Sciences. 246 corn seeds were selected as the research objects, 96 of which were infected with ear rot, and the other 150 were normal samples of the same kind of corn. A Matrix-Ⅰ Fourier NIR spectrometer was used to collect the NIR spectra of the samples in the range of 800~2 500 nm, and the NIR spectra were preprocessed by Multiplicative Scatter Correction (MSC). Four optimal regions were selected combined with the sensitive band of NIR spectrum of organic matter in maize and the absorption peak of the NIR spectrum of samples. Correlation analysis (CA), successive projections algorithm, SPA) and Competitive Adaptive Reweighted Sampling (Competitive Adaptive Reweighted Sampling, Cars), 4 (1 362, 1 760, 2 143 and 2 311 nm), 5 (1 227, 1 310, 1 382, 1 450 nm) were extracted by three characteristic wavelength extraction algorithms with different principles, respectively 1 728 nm) and 10 (1 232, 1 233, 1 257, 1 279, 1 313, 1 688, 1 703, 1 705, 2 302 and 2 323 nm).The characteristic wavelengths extracted were used as input variables of the corn seed ear rot identification model. The disease status of samples was represented by 0-1 (infected normal) as the output true value to establish the support vector machine (SVM) model. The model parameters were optimized by the grid search method and the 10-fold cross-validation method. The results show that the modeling accuracy of the training and test set in three discriminant models, CA-SVM, SPA-SVM and CARS-SVM, is above 90%. The research results in this paper provide a model basis for the maize seed disease diagnosis device. The method of selecting characteristic wavelengths for the optimal region can also provide a reference for establishing other seed disease discrimination models.