Soluble solid content is one of the important indexes for the internal quality analysis of Hami melon. In this study, the prediction model of soluble solid content of Hami melon was established by using near infrared spectroscopy combined with data dimension reduction method. Compared with a variety of spectral preprocessing methods, the second-order derivative was used to process the original spectrum; the preprocessed spectral data were combined with CARS and MC-UVE to extract the characteristic wavelength, and the principal component analysis was used to reduce the dimension; Finally, the spectral data of feature selection and feature extraction were used as the input variables of support vector machine to establish the prediction model of soluble solid content of Hami melon. The results showed that the prediction model established by CARS + SVM was the best, with the correlation coefficient of the model calibration of 0.981 4, and the correlation coefficient of the prediction set was 0.900 2. This model could be used to accurately predict the soluble solids of Hami melon.