Soil organic matter is an important indicator that characterizes soil fertility information, and realizing its rapid and accurate detection can provide effective data support for precision agriculture regional management. The particle size of the soil has a great influence on the spectrum prediction of SOM content and instrument development. To analyze the impact of different particle sizes on SOM prediction, five soil samples with the uniform particle size of 1~2, 0.5~1, 0.25~0.5, 0.1~0.25, <0.1 mm, and mixed particle sizes of <1 mm were prepared, and the visible-near infrared (300~2 500 nm) spectral data was collected. Monte Carlo cross-validation was used to eliminate abnormal samples of different particle sizes, and the spectral data were smoothed and de-noised by the Savitzky-Golay convolution smoothing method. The spectral reflectance differences of samples with different particle sizes were compared, and three spectral transformations were performed on the smoothed original spectrum R, including reciprocal IR, logarithmic LR, and first derivative FDR. The correlation between SOM content and the reflectance of different transformed spectra was analyzed. The characteristic wavelength of the FDR transformed spectral data was extracted based on the Competitive Adaptive Reweighted Sampling (CARS) algorithm. Moreover, combined with the partial least squares regression (PLSR) to establish the corresponding prediction models of SOM content. The results show that the average spectral reflectance and coefficient of variation of soil samples with different particle sizes gradually increase with the decrease of particle size, and the difference is obvious in the wavelength range greater than 540 nm. With the decrease in particle size, the correlation between SOM content, particle size, and spectral reflectance in the whole band range become more obvious. FDR transformation can significantly change the correlation between SOM content and spectral reflectance. The CARS algorithm was used to extract the characteristic wavelengths from the FDR transformed spectral data, and the number of characteristic wavelengths was screened out and reduced to 13.1% of the total number of bands, which reduced the overlap of spectral data and the interference of invalid information. Comparing the results of different SOM prediction models, the FDR transformed spectrum had good modeling accuracy. Especially when the particle size was less than 0.1 mm, the model's $R^{2}_{p}$, RMSEP and RPD value was 0.91, 2.20 g·kg^-1, and 3.33. Among the SOM content prediction models constructed based on CARS characteristic variables, the prediction model with particle size <0.1 mm has the best effect. Its $R^{2}_{p}$ reached 0.78, RMSEP was 3.00 g·kg^-1, and RPD was 2.00, which can achieve reliable prediction of SOM content, and there is still room for optimization of models under other particle sizes. This research can provide a reference for the rapid and accurate prediction of SOM content in the field environment and the design of instruments.