Given the time-consuming and labor-consuming problem of traditional maize stalk strength destructive detection methods, this study used hyperspectral imaging data combined with statistical learning methods to detect the puncture strength and breaking force of the stalks of 19 maize varieties in the filling stage and wax maturity stage. Moreover, the feature extraction and modeling methods suitable for detecting corn stalk strength are given. In the experiment, 19 corn varieties were planted at a planting density of 5 000 plants·mu-1. The hyperspectral images of the base of the stalks at the filling stage and wax maturity stage were collected, and the target area segmentation method was used to automatically perform spectral image reflectance correction and target spectral curve extraction. Principal Component Analysis (PCA) and wrapped feature extraction were used to extract spectral features from the collected sample data, and principal component regression (PCR) and partial least squares regression (PLSR) were developed for the prediction of stalk strength. By comparing each feature extraction method and the cross-validation prediction results of each model, we found suitable feature extraction and modeling methods for maize stalk strength detection. The experimental results showed that the PCA method extracted spectral features had obvious dimensionality reduction effect. However, the PCR model built with PCA method extracted features had average prediction effect on maize stalk strength, and the PLSR model built with wrapped feature extraction method had better prediction effect than the PCR model at both the filling and waxing stages. The residual predictive deviation (RPD) of the PLSR model was higher than that of the PCR model. The RPD of the PLSR model ranged from 2.90 to 3.93, which could be used for quantitative analysis to predict stalk strength.