Visible-near infrared (Vis-NIR) spectroscopy has been proved to be a rapid, timely and efficient tool for predicting content of soil organic carbon (SOC). In this study, FieldSpec4 was used to measure 104 soil samples collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and SOC content were measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method, the multiple scattering correction (MSC) method, after which the spectral reflectance was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) Different spectral preprocessing methods had great influence on the modeling results, and the modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R²=0.86, RMSE=2.00 g/kg, verification R²=0.78, RMSE=1.81 g/kg, RPD=2.69). (2) The correlation curve between reciprocal and SOC content was similar to the correlation curve between the logarithm of the reciprocal and SOC content. They were inversely proportional to the reflectivity curve, and the modeling effect was far lower than the reflectivity; the first-order differential of spectral reflectance could significantly improve the correlation of the 500~600 nm band. (3) The spectral reflectance increased with the decreasing of SOC content. In addition, when the SOC content was low, the sensitivity of the spectrum especially that in the near-infrared band of the original reflectance to the change of SOC content decreased, and the direct modeling difficulty of the reflectance increased.