Abstract: 【Objective】 To explore the accuracy of soil organic matter and total nitrogen estimation model with different modeling methods and to establish a fast and stable estimation model, so as to provide scientific basis for precision fertilization of modern agricultural production. 【Methods】 Taking the cultivated soil from Bortala Mongolia Autonomous Prefecture as the research object, the ASD Field4 ground object spectrometer was used to measure the spectrum of the treated soil samples in the dark room. The original spectrum was processed by breakpoint fitting and Savitzky-Golay (S-G) smoothing filtering correction. First derivative (FD), first derivative of logarithm ((lgR)'), first derivative of reciprocal ((1/R)') and multipication scatter correction (MSC) were performed on the corrected spectrum (R). The correlation analysis of the above five forms of spectra with soil organic matter and total nitrogen content was carried out to screen the characteristic bands. Based on the characteristic bands, partial least squares regression (PLSR), BP neural network (BP) and random forest (RF) were used to establish the estimation models of soil organic matter and total nitrogen, and the accuracy and stability of the models were evaluated. 【Results】 After different transformations, the correlation coefficients between the spectra and soil organic matter and total nitrogen increased, and the characteristic bands were more obvious. The first derivative transformation of the first derivative and the reciprocal was better than those of other transformations. The FD-PLSR model had the highest accuracy in predicting organic matter, with Rv² and RPD of 0.89 and 2.63, respectively. The (1/R)'-PLSR model had the highest accuracy in predicting soil total nitrogen, with Rv² and RPD of 0.83 and 2.42, respectively. 【Conclusion】 Based on hyperspectral technology and machine learning model, the estimation of soil organic matter and total nitrogen in cultivated land of Bozhou can be realized.