For the fast and exact detection of sugar content of fruit vinegar, near infrared (NIR) spectroscopy technique combined with least squares support vector machines (LS-SVM) algorithm was used to build the prediction model of sugar content in the present research. NIR spectroscopy is a nondestructive, fast and accurate technique for the measurement of chemical compo nents based on overtone and combination bands of specific functional groups. The pivotal step for spectroscopy technique is how to extract quantitative data from mass spectral data and eliminate spectral interferences. Principal component analysis (PCA) is a method which has been widely used in the spectroscopic analysis, and LS-SVM is a new data mining algorithm developed from the machine learning community. In the present study, they were used for the spectroscopic analysis. First, the near infrared transmittance spectra of three hundred samples were obtained, then PCA was applied for reducing the dimensionality of the original spectra, and six principal components (PCs) were selected according the accumulative reliabilities (AR). The six PCs could be used to replace the complex spectral data. The three hundred samples were randomly separated into calibration set and validation set. Least squares support vector machines (LS-SVM) algorithm was used to build prediction model of sugar content based on the calibration set, then this model was employed for the prediction of the validation set. Correlation coefficient (r) of prediction and root mean square error prediction (RMSEP) were used as the evaluation standards, and the results indicated that the r and RMSEP for the prediction of sugar content were 0.9939 and 0.363, respectively. Hence, PCA and LS-SVM model with high prediction precision could be applied to the determination of sugar content in fruit vinegar.