Based on frequency spectrum of permittivities from 10MHz to 4500MHz of intact postharvest Fuji apples during 21 weeks storage, BP network model and support vector regression (SVR) model were applied to predict SSC. Effects of the prediction models using full frequency (FF), principal component analysis (PCA) and successive projection algorithm (SPA) were compared and evaluated. The results showed that PCA—SVR gave the best result rather than PCA—BP and SPA—BP. The predicted correlation coefficient of PCA—SVR was 0.883 and the root mean square error (RMSE) was 0.552. The effect of PCA—BP was a little worse than PCA-SVR. The RMSE of the model established by SPA was generally smaller than by other methods, and the predicted correlation coefficient of the models established by PCA was generally higher. The research offered some useful technologies in developing nondestructive sensors for fruits’ soluble solids content based on frequency spectrum of dielectric parameters.