Exploring the relationship between spectral characteristics and nutrient content of the grassland communities is of great significance for promoting the application of rapid non-destructive testing technology in grassland fertilization management, which can be used to diagnose the nutritional status of the grassland communities by hyperspectral technology. A typical grassland community in the Loess Hilly-gully region on the Loess Plateau, Bothriochloa ischaemum community, was investigated with treatments of four nitrogen (N) addition (0, 25, 50, and 100 kg·N·ha-1·yr-1) and four phosphorus (P) addition treatments (0, 20, 40, and 80 P2O5·kg·ha-1·yr-1). Based on hyperspectral and community N and P nutrient content measurements, combined with the first derivative treatment in the red-edge region, 18 characteristicspectral parameters consisting of vegetation indexes, characteristic bands and red-edge parameters, the characteristicspectral parameters sensitive to the N and P content and N∶P ratio of B. ischaemum community were screened by multiple linear stepwise regression (SWR) methods, and an inverse model was established to estimate the aboveground total N content and total P content and N∶P ratio in the community. Results showed that the N and P content of the B. ischaemum community increased with N application, and the N∶P ratio decreased with P application; the spectral reflectance under N and P addition is inversely proportional to fertilizer application in the visible band and positively proportional to fertilizer application in the near-infrared band, and the “double-peak phenomenon” of the first derivative in the red-edge region was significantly affected by N and P addition. Among them, TBSI, R910 and AMP contributed significantly to the model for N estimation (R2=0.87, F=18.8***), while DVI, mSR705, R430, R660 and AMP contributed significantly to the model for P estimation (R2=0.91, F=20.51***), and Slope725 contributed the most to the model for the estimation of N∶P ratio (R2=0.54, F=5.14***). This study used hyperspectral technology to achieve a rapid estimation of the N and P content of the B. ischaemum community, and based on the significant correlation between N and P content and N∶P ratio and spectral feature parameters, the parameter combination with the highest accuracy was selected, which laid a foundation for the method and parameter selection of monitoring grassland nutrient content after N and P addition at large scale.