Abstract: Rapid and accurate estimation of soil nutrient content based on hyperspectral data is an optimal method for the monitoring of soil nutrient and inversion of soil physical and chemical characters. The relationship between soil nutrient content and spectral reflectance was analyzed with soil samples being collected from the loess hilly-gully region of northern Shaanxi Province. The prediction models of the content of soil organic matter, total nitrogen, total phosphorus and total potassium were constructed by the combination of three techniques, including partial least squares (PLS), multiple linear regression (MLR), and support vector machine (SVM). Then, the optimal model was selected by comparison analysis. The results showed good correlations between the content of soil nutrients and spectral reflectance in visible region (400-760 nm) and near infrared region (760-1100 nm). The maximum values of correlation coefficient located in both spectral regions. The SPA-SVM model had the best applicability and highest inversion accuracy for the contents of all soil nutrients, with simple and efficient modeling process. Our results provided a reference for applying machine learning algorithm in the construction of hyperspectral prediction model of soil nutrient content in the loess hilly-gully region.