Abstract: Soil organic matter (SOM) is one of the essential components of soil and moisture to interfere with the detection. The soil moisture content by mass ratio and spectral parameters are often utilized to classify the moisture levels. But previous studies on the prediction of SOM content have focused mostly on the surface soil without covering the profile of depth range. This study aims to explore the influence of soil moisture on the inversion accuracy of the SOM profile. This study analyzed previously obtained near-infrared spectra were collected from the samples of the soil core column in the surface layer to about 150 cm underground. Each core was divided into subsamples with a height of 10 or 20 cm. The samples were gradually moistened to 6 groups of levels of soil moisture. According to the index of moisture tension, the air-dry state was defined as 1.500, 0.330, 0.100, 0.033 MPa, and the saturated state in turn. The data was normalized and transformed to the absorbance. Each set of spectral data was processed by seven spectral preprocessing. Among them, the standard normal variate transformation (SNV) was achieved the best. Meanwhile, successive projection algorithms (SPA) and competitive adaptive reweighting-successive projection algorithms (CARS-SPA) were used to screen the characteristic wavelengths. The number of characteristic wavelengths was then reduced to 6-8 at each level of soil moisture. The inversion models of SOM were constructed to combine with SNV preprocessing using full spectrum and characteristic wavelengths. The results indicated that: 1) The model accuracies of the SPA-PLSR and CARS-SPA-PLSR models were better than that of the PLSR model at six levels of moisture. 2) The SNV preprocessing was also combined to determine the coefficient of determination in prediction (R²_p) and root mean square error of the prediction set (RMSEP). Under the saturated state, R²_p values of the SNV-SPA-PLSR and SNV-CARS-SPA-PLSR models were 0.664 and 0.651, respectively, while the RMSEP values were 1.095 and 1.131 g/kg, respectively. In the air-dry state, R²_p values of the two models were 0.799 and 0.753, respectively, and RMSEP values were 0.759 and 0.848 g/kg, respectively, indicating the better prediction of the SNV-SPA-PLSR model. The SNV-CARS-SPA-PLSR model shared the higher accuracy of prediction when the moisture tension levels were 0.033, 0.100, 0.330, and 1.500 MPa. Specifically, R²_p values increased from 0.699 to 0.846, whereas, the RMSEP values decreased from 1.013 to 0.620 g/kg. 3) However, it was difficult to guarantee the same soil moisture level at the same depth in different locations of the field. The SOM calibration model was applied to the characteristic wavelengths on different datasets. The SNV-CARS-SPA-PLSR model was selected at the moisture tension of 1.500 MPa. The best performance was achieved for the organic matter in the six groups of soil moisture levels and mixed samples. The models can be expected to estimate the SOM content of the profile at various moisture levels. The findings can also provide a strong reference to improve the applicability of near-infrared spectra inversion models for the organic matter content at different levels of soil moisture.