Soil particle size is one recognized factor that cause serious interference to the Near-Infrared (NIR) spectroscopy. Generally, grinding and sieving soil are used to reduce soil particle size interference in the sample pre-processing stage. Mathematical methods such as the continuous spectrum derivative method are used to eliminate soil particle size interference in the data processing stage. However, for the discrete NIR spectral data, so far, there is still no effective methods to eliminate the interference of soil particle size. In this paper, the discrete NIR absorbance data of the soil samples are taken as the research object, to solve the problem of soil particle size interference elimination, and a soil particle size correction method is proposed. Firstly, establishing a soil particle size correction model. After drying the standard soil samples collected from the field to eliminate the interference of soil moisture, the soil samples are prepared. Finally, a total of 96 soil samples under four soil particle sizes (2.0, 0.9, 0.45, 0.2 mm) and six soil total nitrogen (TN) concentration levels (0, 0.04, 0.08, 0.12, 0.16 and 0.2 g·kg^-1) were obtained. Calculating the standard deviation of four different particle sizes (each particle size contains 24 soil samples) and all 96 soil samples at each wavelength (850~2 500 nm), and the 1 361 nm and 1 870 nm were confirmed to be the characteristic wavebands of the soil particle size. The characteristic wavebands ratio was used as a single input variable to establish the SVM soil particle size classification model, and the overall classification accuracy of soil particle size was 93.8%. The results showed that it was feasible to use to classify the soil particle size. Based on the above results, a soil particle size correction method is proposed to eliminate the interference of soil particle size to the discrete NIR spectral data. Our team selected the six discrete NIR wavebands (1 070, 1 130, 1 245, 1 375, 1 550, 1 680 nm) using in the TN detector developed by our team to verify the soil particle size correction method proposed in this paper. The results showed that the corrected 2.0, 0.9, 0.45 mm and original soil absorbance were reduced by 62%, 74%, 111% and 61%, respectively. It showed that the soil particle size correction method could reduce the interference of soil particle size to discrete NIR spectral data. Finally, BPNN was used to establish the TN models with different absorbance data. The results showed that the R_v² of the corrected soil absorbance model was improved by 25% compared with the original absorbance model. In summary, the soil particle size correction method proposed in this paper reduce the interference of the soil particle size on the discrete NIR spectral data, and improve the detection accuracy of the vehicle-mounted TN detector.