The sugar content and the matrix always are being changed during cider-making fermentation.In order to measure and monitor sugar content accurately and rapidly,it is necessary for the spectra to be sorted.Calibration models were established at different fermentation stages based on near infrared spectroscopy with artificial neural network.NIR spectral data were collected in the spectral region of 12000-4000cm^-1 for the next analysis.After the different conditions for modeling sugar content were analyzed and discussed,the results indicated that the calibration models developed by the spectral data pretreatment of straight line subtraction(SLS) in the characteristic absorption spectra ranges of 7502-6472.1 cm^-1 at stageⅠand 6102-5446.2 cm-1 at stageⅡ were the best for sugar content.The result of comparison of different data pretreatment methods for establishing calibration model showed that the correlation coefficients of the models (R2) for stageⅠand Ⅱ were 98.93% and 99.34% respectively,and the root mean square errors of cross validation(RMSECV) for stageⅠandⅡ were 4.42 and 1.21 g·L^-1 respectively.Then the models were tested and the results showed that the root mean square error of prediction (RMSEP) was 4.07 g·L^-1 and 1.13 g·L^-1 respectively. These demonstrated that the models the authors established are very well and can be applied to quick determination and monitoring of sugar content during cider-making fermentation.