Soil organic matter content (SOMC) is an important parameter that reflect soil fertility available for crop production, and monitoring of the SOMC dynamically has shown great importance to promote the development of precision agriculture. In recent years, many researchers have tried to use proximal soil sensing, especially using the proximal hyperspectral techniques to acquire different kinds of spectral data under the field and laboratory conditions, and various new algorithms are also introduced to build inversion models to predict SOMC from spectra for different regions and different kinds of soils. In this paper, the hyperspectral reflectance of different soil samples was measured using the ASD FieldSpec3 spectrum analyzer. At the same time, the SOMC of each soil sample was analyzed using potassium dichromate external heating method in the laboratory. The correlation analyses between raw soil spectral reflectance (R) and SOMC were done, and it could select sensitive wavebands reflectance when the determination coefficients (R2) exceeded 0.15. A continuous wavelet transform (CWT) was also performed on R and the continuum removal curves (CR) to generate a wavelet power scalogram in different scales, the correlation analyses were done between wavelet power coefficients and SOMC, and it could select the sensitive wavelet coefficients when the R2 exceeded 0.3. Then, after extracting wavebands reflectance from R and wavelet power coefficients from R-CWT, CR-CWT, the estimation models for SOMC had been successfully built by partial least squares regression (PLSR), BP neural network (BPNN), support vector machine regression (SVMR), respectively. The results showed that, compared to the R2 between SOMC and R, the R2 between SOMC and R-CWT, CR-CWT wavelet coefficients were increased by about 0.15 and 0.2. The CR-CWT-SVMR model was the best, its R2, RMSE and RPD value of validation set were 0.83, 4.02, 2.48, which could estimate SOMC comprehensively and stably. For the CR-CWT-PLSR model, although there was a slight gap in the prediction accuracy with that CR-CWT-BPNN and CR-CWT-SVMR models, it also had its own unique advantages: the model was simple and thus the computation speed was reduced significantly. In the future, the results can provide good potential for field proximal sensing researching.