Whether a finer soil classification hierarchical stratification strategy and the spectral characteristic parameters (SCPs) that describe the shape of the spectral curve can be used to improve the prediction accuracy of soil organic matter should be clarified. We measured the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 400 - 2500 nm) spectral reflectance of 322 topsoil samples. The spectral reflectance was converted to continuum removal curves, and then, SCPs were extracted based on the curves. According to the results of the Second National Soil Survey of China, the samples were divided into 4 great groups or 8 genus, and a variety of stratification strategies were constructed based on great group (GR-S), genus (GE-S), spectral similarity (SS-S) and decision tree model (DT-S). A local random forest model was established to evaluate the performance of different stratification strategies and input variables. Our results are described as follows: (1) In different stratification strategies, the SOM prediction model based on DT-S exhibits the highest accuracy, followed by the SOM prediction models based on GE-S and SS-S; the SOM prediction model based on GR-S exhibits the lowest accuracy; (2) among the different input variables, the root mean squared error (RMSE) and coefficient of determination (R2) of the best SOM model predicted by SCPs are 5.18 g kg(-1) and 0.89, respectively. Compared with the original reflectance based on the nonstratified strategy, the RMSE decreases by 4.88 g kg(-& nbsp;1) and R(2 )increases by 0.32. The study results highlight the advantages of refining the soil hierarchy, which is helpful for identifying the differences in soils at the regional scale and analysing the relationship between stratification results and the characteristics of the soil environment to obtain a highly accurate prediction model.