Given the time-consuming, labor-intensive and time-lagging problems of traditional methods for identifying lodging resistance of maize, this study uses hyperspectral imaging data combined with machine learning methods to identify lodging resistance of maize at the 9-leaf stage. It gives the recommended planting density and modeling methods. The experiment set up 3 planting densities of 5 000, 7 000 and 9 000 plants·mu^-1 and 6 typical lodging resistant/non-lodging resistant varieties. Hyperspectral images of corn top leaves at the 9-leaf stage were collected, reflectance correction and target spectral curve extraction were automatically performed by segmentation of the target area. For the collected sample data, the Kennard Stone algorithm is used to divide the sample training set and the test set, the principal component analysis (PCA) and the successive projections algorithm (SPA) are used to extract the spectral features. A support vector machines (SVM) model of Gaussian kernel function is established, with the performing of parameter training and optimization. By comparing the effect of each feature extraction method and the training effect of each model, and its prediction results under different planting densities, the planting density and modeling method recommended for the identification of maize lodging resistance were found. The test results show that the PCA method has the most significant dimensionality reduction effect on the spectral features at various planting densities. At the same time, the characteristic wavelength distribution selected by the SPA algorithm is relatively uniform, and the lodging resistance classification characteristics are obvious. The increase of planting density is beneficial to identifying the lodging resistance of maize. When the planting density is 7 000 plants·mu^-1, the training effect and prediction results of the model established by the SPA-SVM method are the best. The 10-fold cross-validation accuracy of the model on the training set data is 97.40%. The prediction accuracy rate of the set data is 98.33%.