Rapid and non-destructive testing of soybean stress environment are critical to improving soybean quality and yield. In recent years, the detection of plant stress by hyperspectral technology has been widely used, but there are few reports on the application of water and nitrogen stress in soybean. Four kinds of water and five kinds of nitrogen levels were set in the flowering and pod-forming soybeans for stress experiments in this paper. After the stress, the physiological information data of hyperspectral, chlorophyll content and net photosynthetic rate were obtained, and 15 spectral vegetations indices were calculated by spectral data. The index NDVI, RVI, GNDVI, mNDVI705 and LCI were used to indicate the effects of water and nitrogen stress on soybean. And soybean physiological information was predicted by establishing single leaf chlorophyll content and net photosynthetic rate inversion model. The sensitive region was extracted by correlation analysis, and they were 520～622 and 485～664 nm respectively. Multivariate scatter correction (MSC), standard normal variable transformation (SNV), first derivative (FD), second derivative (SD) and Savitzky-Golay smoothing (S-G) preprocessing are used, while two modeling methods, principal component regression (PCR) and partial least squares (PLS), are selected to combine them into several methods according to a certain relationship. The correlation coefficient is used as a model evaluation index to find a combination of optimal preprocessing and modeling methods. The results showed that the hyperspectral curves of non-stressed and stressed soybeans had the same trend but different spectral reflectance values. The reflectance of unstressed soybean has the lowest value in the 500～700 nm region and the highest value in the 760～900 nm region, and the reflectance in the 500～700 nm region gradually increases with the increase of the degree of water-nitrogen stress. The effects of different water and nitrogen levels on vegetation index were different, but the changes were consistent. The 5 vegetation indexes showed that the unstressed soybean was larger than the stressed soybean, and the vegetation index value decreased with the increase of the degree of water-nitrogen stress. The optimum combination of inversion models is MSC+FD+S-G+PLS and SNV+SD+S-G+PLS. The correlation coefficients of the correction set are 0.960 6 and 0.992 7, and the correlation coefficients of the prediction set are 0.972 0 and 0.970 8, respectively. The results show that the model has high precision, and can accurately predict the physiological information of chlorophyll content and net photosynthetic rate of stressed and unstressed soybean, and provide technical support for detecting physiological information during large-scale planting.