The hyperspectral estimation of soil organic matter content can quickly and accurately monitor soil fertility and provide a scientific basis for proper fertilization of modern agricultural production. Taking the cultivated soil in the delta oasis of Weigan-Kuqa river, Xinjiang as the research object, the original spectral reflectance (R) of the collected 98 soil samples was subjected to the traditional reciprocal logarithm lg(1/R), the first-order differential (R') and the reciprocal logarithm first-order differential [lg(1/R)]' mathematical transformations, and continuous wavelet transformation (Continuous Wavelet Transformation, CWT) processing based on Bior1.3 as the wavelet mother function through different scale decomposition. Correlation analysis was conducted between the treatment results and the measured soil organic matter content to screen out the characteristic bands and wavelet coefficients closely related to soil organic matter content under various transformations (p<0.01). With the original spectral reflectance, the characteristic band reflectance and the sensitive wavelet coefficient under different transformation treatments as independent variables and the soil organic matter content as dependent variables, partial least squares regression and support vector machine regression was used to estimate models of soil organic matter content. The results showed that: (1) Various spectral transformation methods can effectively improve the sensitivity between the spectrum and the content of soil organic matter. The correlation between the soil spectral reflectance and the organic matter content after continuous wavelet transformation has been significantly improved, and the correlation coefficient has been increased from 0.39 to 0.54 (p<0.01). (2) The support vector machine regression model built by the traditional [lg(1/R)]' transformation has a higher coefficient of determination (R²) than the model built by lg(1/R) and R' transformation, showing the reciprocal logarithm first-order differential transformation can help improve the accuracy of the estimation model, and the accuracy and stability of the support vector machine regression model were higher than that of the partial least squares regression model. (3) After continuous wavelet transformation decomposition, the estimation accuracy and stability of the models were obviously improved by using the sensitive wavelet coefficients of the original spectral reflectance at different scales as independent variables. The decision coefficient (R²), root mean square error (RMSE) and relative analysis error (RPD) of the CWT-23-SVMR model were 0.84, 1.48 and 2.11 respectively. The model has the highest accuracy and excellent predictive ability. After multiple transformation processing, hyperspectral data can effectively remove white noise. In contrast, continuous wavelet transformation processing is more suitable for mining effective soil information than the traditional mathematical transformation method, to realize the effective separation of approximate features and detailed features of spectral signals, and the established inversion model can more accurately estimate soil organic matter content.