It is possible to provide theoretical basis for accurate identification and quantitative extraction of water quality indicators remote sensing spectroscopy by studying the variation of water quality spectrum and its spectral characteristics under different target concentrations with Hyperspectral technology, which has been widely used in water quality testing. A total of 47 typical stations in Langya Mountain Scenic Area were selected for water quality testing and spectral synchronization in this paper. Then, seven water quality indexes and 350~950 nm bands of each test point were extracted to explore the variation of spectral characteristics of different concentration water quality indexes, and to analyze the relationship between water quality index and spectral reflectance, first derivative reflectance, any two-band reflectance ratio and difference. The results showed that the spectral curves of the water quality indexes were consistent, but their changing rules were different.What’s more, bands with the highest degree of discrimination were in the visible range. The spectral curves of water quality with different salinity, total dissolved solids and conductivity content were close to each other, and reflectivity of water samples that change the most significantly was the highest. The spectral reflectance of the water samples with higher turbidity content was more obvious, but there was no difference of spectral reflectance of different turbidity content samples in the range of 700~950 nm. The spectral reflectance of water with a dissolved oxygen concentration of 4~49 mg·L-1 was significantly lower than that of the remaining samples in the range of 350~900 nm. In the range of 350~380 nm, the spectral reflectance did not change with the chlorophyll content, and the samples with chlorophyll content close to zero were significantly lower than those of the remaining samples in the 400~950 nm bands. The spectral curves of different BGA-PC concentrations water samples were more complex than other water quality indexes in the range of 350~730 nm. In addition, the correlation between the water quality index and its original spectral reflectivity was low, but it could be improved by the combination of band reflectivity, such as the first derivative reflectance, any two-band reflectance ratio and difference. This study aims at providing a theoretical support for water quality monitoring of hyperspectral remote sensing.