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Hyperspectral remote sensing technology is a powerful tool in the analysis of soil compositions as well as soil physical and chemical properties. Totally 385 natural soil samples were collected from cotton fields in North Xinjiang Province, the selected soil samples according to the total nitrogen content were processed by 2mm, 1mm, 0.5mm and 0.15mm sieves, and their spectral reflectance characteristics were measured. After the transformation of spectral data with twelve forms, the spectral inversion models of soil nitrogen content were established based on support vector machine (SVM), partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR), and the accuracy and universality of the model were tested. The results showed that there was no significant correlation between the original spectral characteristics and soil nitrogen content, and which can be improved by different data transformations. In the same data transformation, there was no obvious difference in the band position corresponding to the maximum correlation coefficient in different particle size processing. According to the fitting accuracy of different particle size treatments, the smaller the particle size of the sieve was, the higher the precision of the total nitrogen content was, the optimal fitting models of the three methods were all processed by 0.15mm sieve treatment, the SVM method used (lgR)′ transformation, the model R2c was 0.8987, the RMSEc was 0.0181 and the RPD was 2.7049, the PLSR and the SMLR methods used R′ transformation, the R2c were 0.8520 and 0.8196, the RMSEc was 0.0413 and 0.0436, and the RPD was 2.5549 and 2.4374, respectively. The optimal model was checked with the samples which were not involved in building model and the R2 of SVM, PLSR and SMLR were 0.8829, 0.7715 and 0.7054, respectively. From the prediction error of the model, the lower the soil total nitrogen content was, the greater the prediction error was, it was impossible to accurately estimate the soil total nitrogen content by spectral reflectance characteristics.