The Soluble solid content in grapes is an important indicator for evaluating grape ripeness, and this paper explores the quantitative analysis of soluble solid content (SSC) content in several varieties of grapes (Hongti, Jufeng, and Liaofeng) based on visible/near-infrared (NIR) spectroscopic techniques. The transmission spectra of three grape varieties in the wavelength range of 550-960 nm were collected separately, and Savitzky-Golay convolutional smoothing (S-G), standard normal variate (SNV), wavelet transform (WT), and the combination of first-order derivation + S-G convolutional smoothing (1stDer+S-G) were used to analyze the soluble solids content of the grapes. Preprocessing methods, and compare and analyze the most suitable preprocessing methods for each variety; then under the optimal preprocessing methods, we used the continuous projection algorithm (SPA) and competitive adaptive reweighting (CARS) to select the characteristic wavelengths of the spectra; and combined with chemometrics methods to establish the partial least squares regression (PLSR) for multi-species and single-species, and the lossless prediction model for the content of the SSC of the BP neural network, respectively. The results showed that the SSC content model based on BP-SPA was optimal, and the prediction set correlation coefficient (Rp2) of the generalized SSC content prediction model for multiple varieties was 0.85, which indicated that the non-destructive detection of SSC content in multiple grape varieties based on visible/near-infrared spectroscopy was feasible.