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Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time-consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, Shandong Peninsula, China were measured by an ASD FieldSpec 3 portable object spectrometer. Raw spectral reflectance datewas transformed by four methods. These were: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA+FD), NWMA withstandard normal variate (NWMA+SNV), and NWMA with min-max standardization (NWMA+MS). These data were analyzed and correlated with SOM content. The evaluation model was built by support vector machine regression (SVM) using sensitive wavelengths. The results showed thatNWMA+FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA+FD method were 917 nm, 991 nm, 1007 nm, 1996 nm, and 2267 nm. The SVM model established by the above five sensitive wavelengths was significant (R c 2 = 0.875, RMSE c = 0.107g·kg −1 , R v 2 = 0.853, RMSE v = 0.097g·kg −1 ). The results indicated that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients.