Soil moisture determines a region’s ecological carrying capacity and soil physical and chemical properties to a certain extent. It is significant to obtain soil moisture content accurately and quickly for ecological environment monitoring and soil degradation restoration. Hyperspectral remote sensing is widely used in soil parameter inversion, but the research on hyperspectral characteristics and parameter inversion of alpine meadow soil needs further study. Consequently, to develop a hyperspectral inversion model of soil moisture content in alpine meadows applicable to fragile alpine ecosystems, 102 soil samples were collected from Henan County in the Yellow River source area. Multiple linear stepwise regression (MLSR), partial least squares regression (PLSR) and back propagation neural network (BPNN) methods were used to model the soil moisture content with the original spectrum and its mathematically transformed characteristic bands, and the inversion accuracy was verified by the coefficient of determination (R2), root mean square error (RMSE) and the residual ratio of prediction (RPD). The major findings are as follows: (1) In the visible-near infrared band, the spectral reflectance of soil samples has water absorption interval near 710, 780 and 950 nm, and the absorption intensity is different. The reflectance tends to decrease rapidly and increase slowly with increasing soil moisture content. (2) SPA algorithm was used to select the spectrum’s characteristic bands after S-G smoothing, four transformations as independent variables and water content as dependent variables. Then MLSR and PLSR were used to establish the inversion model. The PLSR model corresponding to the first-order differential (FD) and first-order logarithmic differential (FDL) transformations can achieve a rough inversion of soil moisture in alpine meadows, and the PLSR model corresponding to the FD transformation is accurate. (3) In the BPNN inversion models, except for the model corresponding to continue to remove (CR), theR2 of other models is greater than 0.9, and RMSE is between 0.048 and 0.074. In all the models, the BPNN model corresponding to FD, FDL and LG transform is highly accurate, with R2 and RPD greater than 0.8 and 2.5 respectively. The BPNN model corresponding to the LG transform has the highest accuracy, with R2, RMSE and RPD up to 0.967, 0.038 and 5.039, respectively. Therefore, the BPNN model can achieve relatively accurate hyperspectral inversion of soil moisture content of alpine meadow in the source region of the Yellow River, which can provide the technical basis and data support for ecological environment monitoring and soil restoration in this region and even other alpine regions.