In order to detect corn varieties quickly, a classification model of corn varieties was established based on the combination of support vector machine (SVM) and near-infrared spectroscopy. 293 samples from five varieties, including Zhengdan 958, Xianyu 335, Jingke 968, Denghai 605 and Demeiya, were collected as research objects. After performing standard normal variable transformation (SNV) processing on the collected near-infrared spectra, the principal component analysis (PCA) method is used to reduce the dimensionality of the spectral data. According to the ratio of 6:1, 251 samples were randomly selected as the training set and 42 samples as the test set to explore the influence of the Bayesian optimization (BO) algorithm on the performance of the SVM model. Three methods, including grid search(GS), genetic algorithm(GA) and BO algorithm, were used to optimize the two important parameters of the SVM model, namely, the penalty factor C and the radial basis kernel function parameter γ. The C and γ, corresponding to the highest recognition accuracy based on ten-fold cross-validation of each model, were used as modeling parameters, and the SVM classification model based on the three optimization algorithm methods were established. The SVM classification model based on BO is compared with the model based on GS and GA. The experimental results show that the performance of the SVM classification model optimized by BO is superior to that of the other two optimization algorithms, and the recognition accuracy on the test set can reach 100%. This shows that the parameters of the SVM model optimized by BO are the optimal global parameters, and the parameters optimized by the other two optimization algorithms may fall into the local optimal, resulting in poor performance of the model. BO-SVM models were established on the spectral data before and after PCA dimensionality reduction. The results show that BO is not good for high-dimensional data optimization, and it is more suitable for low dimensional data. For the problem of poor performance of the model caused by the imbalance of the number of different sample categories, the SVM models were re-established by removing the two small samples, Zheng Dan 958 and Xianyu 335, and using the remaining three categories, a total of 248 corn samples. The experimental results show that the performance of each model on the test set is improved after removing the two types of small samples, which indicates that for the problem of unbalanced sample number between classes, the more samples of a certain class, the more delicate the correction of model parameters, and the better the fitting effect of the model on this class. The results of this study can be used for rapid identification of corn varieties and can also provide references for the classification and origin identification of other agricultural products based on near-infrared spectroscopy.