Hyperspectral technique was applied to detect polyphenol oxidase (PPO) content in cauliflower with early botrytis stress. A total of 253 healthy cauliflower samples and 257 infected cauliflower samples were used to acquire hyperspectral within the range of 900~1 700 nm, and the corresponding PPO content in the cauliflowers were measured with the spectrophotometry method in order to make the prediction effect better. The mean value was applied to the analysis of the PPO with cauliflower samples, and results showed that the mean PPO content of healthy cauliflower (10.257 U·g-1) was less than that of infected cauliflower (12.324 U·g-1). The SPXY method divides the cauliflower sample set into a calibration set (193 healthy, and 197 infected samples) and a validation set (60 healthy and 60 infected samples). Six kinds of single pretreatment were performed on the divided sample set. The R (correlation coefficient) and RMSE (root mean square error) were used as the model evaluation index, and results showed that pretreatment can effectively improve the accuracy and stability of the mode. It was found that the predictive set modeling effect of healthy samples after NOR pretreatment is the best and that of infected samples after DT pretreatment is the best. Successive projection algorithm (SPA) and regression coefficient (RC) were used to select the characteristic wavelengths. Partial least squares regression, least squares support vector machines, and BP neural networks were built to explore the impact of different feature wavelength extraction methods on the accuracy of the model and compare the accuracy of different modeling methods on the prediction of PPO content of cauliflower. The results showed that extracting the characteristic wavelength can optimize the spectral information, and the number of wavelengths extracted by SPA and RC for the two samples were 9, 12, 7 and 11 respectively. It was found that the LS-SVM model has a good fitting effect on the two samples and their corresponding enzyme activities by comparing and analyzing the effect of the model. It was found that the LS-SVM model had a good fitting effect on the two samples and their corresponding enzyme activities. Finally, the results showed that the SPA-LS-SVM model had a good prediction effect on the PPO content of healthy cauliflower, with an Rp (correlation coefficient of prediction) value of 0.832 and an RMSEP (prediction root mean square error) value of 1.676; and RC-LS-SVM model had a good prediction effect on PPO content of infected cauliflower, with a Rpvalue of 0.848 and a RMSEP value of 1.156. This study showed that the hyperspectral technique can detect PPO content in cauliflower with botrytis stress and provide a theoretical basis for rapid detection of PPO contentin cauliflower and the development of portable instruments.