Soil organic carbon (SOC) is one of the most important factors affecting soil fertility. Due to the characteristics of highly aromatic carbon structure and developed pore structure, biochar can be used as a soil amendment to increase SOC content and improve soil physical structure, which has become a research hotspot in the fields of agriculture and environment in recent years. In this study, both traditional method and visible near-infrared spectroscopy (VIS-NIRS, 400-2500 nm) were used to detect SOC content in samples containing different amounts of biochar, in order to establish an effective prediction model for the analysis of organic carbon in soils containing biochar. An optimal prediction model was established for quantifying SOC content through three processes, including comparing different sample-selection methods (Kennard-Stone, Random selection, and SPXY), comparing various spectral pre-processing methods, and matching with three models. The pre-processing methods included Savitzky-Golay smoothing (SG), log(1/R), standard normal variate transformation (SNV), first derivative (Der1), second derivative (Der2), and multiplicative scatter correction (MSC). The three models applied in this study were Synergy Interval Partial Least Squares (siPLS), Genetic Algorithm-Support Vector Machine (GA-SVM), and Random Forests (RF). Results showed that: (1) SOC content was increased significantly by biochar addition and was affected by the amount of biochar. (2) Soil reflectance decreased with the SOC content increasing, indicated by obvious absorption valleys at the spectra nearby 1410, 1920, and 2200 nm. (3) Compared with the three sample selection methods, the sample set divided by KS method was more suitable for the SOC modeling process than those by RS and SPXY methods. (4) The model established by SG+MSC pretreatment combining with GA-SVM method had the highest accuracy, with R_cal²=0.9526 and RMSECV=0.4839 in the calibration set, and R²_val=0.8598, RMSEP=0.9987, and RPD=2.6017 in the validation set. The model can be used for scientific prediction of SOC in samples containing biochar due to its advantages of high precision and good simulation effects.