【Objective】 The purpose of this study is to explore how to rapidly predict soil organic matter in red soil so as to meet the needs of smart agriculture and precision fertilization. 【Method】 This paper took the northern part of Fengxin County in the Northwest Jiangxi Province as its research area and used the 1 km×1 km standard grid method to divide the study area for soil sampling. A total of 248 red soil samples were collected and dried for spectral measurement. Three different mathematical transformation methods, including fractional order derivatives, were used to analyze the soil spectra. In the tests the 350~399 nm and 2 451~2 500 nm bands were removed because they were very susceptible to environmental noises. And noises in the remaining bands were removed with Daubechies(DB) wavelet. Then samples were collected from the pretreated spectral bands at 10 nm intervals to form a 205-band so as to reduce data dimensions and data redundancy. The 800~1 000 nm band, which was liable to the impact of iron oxide, was ruled out of the experiment. The bands used to construct the model were filtered by the P=0.01 significance test. A model was built up with the partial least squares regression (PLSR) in combination with BP neural network for prediction of soil organic matter content. And the model was tested. 【Result】 Results show that the prediction using the PLSR-BP composite model was the best after the soil spectral data was transformed with the 1.5 order fractional derivative, with R²=0.89 and RMSE=4.68 g∙kg^-1 for the training dataset and R²=0.87, RMSE=5.55 g∙kg^-1 and RPD=2.75 for the validation dataset. 【Conclusion】 The transformation of red soil spectral data with the 1.5 order fractional derivative better highlights characteristics of organic-matter-related information, which is helpful for prediction of organic matter contents. And the PLSR-BP composite model is higher than any single models in prediction accuracy, and can be used to predict organic matter content in red soil very well. So it can also serve as a new approach to predicting quickly organic matter content in red soil for precision agriculture.