In this study, 96 surface soil samples are obtained from the typical oasis of the Ugan-Kuqa River in the Xinjiang Uyghur Autonomous Region and their spectral reflectance and soil organic carbon (SOC) content are evaluated. Using fractional order differential technique (with an order value range of 0-2 and a step size of 0.2) is combined with five machine learning algorithms, including the extreme learning machine, random forest, multiple adaptive regression spline function, elastic network regression, and gradient lifting regression tree (GBRT) algorithms, and high-precision estimation of SOC content. The experimental results show that the pretreatment effect obtained using a fractional order differential is better than that obtained using an integer order differential. The correlation at a specific band is significantly improved, and the maximum correlation is enhanced by approximately 0.220. In case of the GBRT, the verification concentration determination coefficient is 0.878 and the relative analysis error is 3.142, indicating that this type of integrated learning is superior to other models of different orders. GBRT based on a 1.6-order spectral reflectance should be used to estimate the SOC content of the oasis in arid areas. Thus, a new scheme based on the combination of visible light-near infrared(VIS-NIR)with the fractional order differential technology and machine learning algorithms is proposed in this study to improve the accuracy of the model used for estimating the SOC content of the oasis in arid areas.