Soluble solids content (SSC) is a key index to evaluate the quality of strawberries. In order to achieve the non-destructive evaluation of SSC, the near infrared spectroscopy was used to build the linear partial least squares (PLS) and nonlinear least squares support vector machine (LS-SVM) models. Twenty-seven effective variables were selected from the original 4 254 variables by combining both successive projections algorithm and Monte-Carlo uninformative variable elimination. The quantitative analysis models were then established by using the selected effective variables. At the same time, color feature parameters were obtained based on components of RGB images of samples considering the influence of surface color on strawberries, and the multi-parameter PLS and LS-SVM models were constructed by further integrating spectra and color features. Based on the same correction set and prediction set, the prediction performance of all models for SSC was compared. The results showed that MC-UVE-SPA was an effective spectral variable selection algorithm, and the multi-parameter fusion nonlinear LS-SVM model was the optimal model for the quantitative prediction of SSC in strawberries. For samples in the prediction set, the correlation coefficient (RP) and root mean square error of prediction (RMSEP) of the model were 0.9885 and 0.1532, respectively. This study lays a foundation for the development of portable instruments and online detection equipments for the detection of soluble solids content in strawberries based on near infrared spectroscopy.