In order to classify and set different prices on basis of difference of beet sugar content in the acquisition process and promote the development of beet sugar industry healthily, a fast, nondestructive, accurate method to detect sugar content of beet was determined by applying near infrared spectroscopy technology. Eight hundred twenty samples from 28 representative varieties of beet were collected as calibration set and 70 samples were chosen as prediction set. Then near infrared spectra of calibration set samples were collected by scanning, effective information was extracted from NIR spectroscopy, and the original spectroscopy data was optimized by data preprocessing methods appropriately. Then partial least square(PLS)regression was used to establish beet sugar quantitative prediction mathematical model. The performances of the models were evaluated by the root mean square of cross-validation (RMSECV), the coefficient of determination (R2) of the calibration model and the standard error of prediction (SEP), and the predicted results of these models were compared. Results show that the established mathematical model by using first derivative(FD) and standard normal variate transformation(SNV) coupled with partial least squares has good predictive ability. The R2 of calibration models of sugar content of beet is 0.908 3, and the RMSECV is 0.376 7. Using this model to forecast the prediction set including 70 samples, the correlation coefficient is 0.921 4 between predicted values and measured values, and the standard error of prediction (SEP) is 0.439, without significant difference (p＞0.05) between predicted values and measured values. These results demonstrated that NIRS can take advantage of simple, rapid, nondestructive and environmental detection method and could be applied to predict beet sugar content. This model owned high accuracy and can meet the precision need of determination of beet sugar content. This detection method could be used to classify and set different prices on basis of difference of beet sugar content in the acquisition process.