In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectral data of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves’ spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then those characteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of RC=0.970, RCV=0.965, RP=0.934, meanwhile evaluation parameters of NC predicted model were RC=0.857, RCV=0.806, RP=0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflect the NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC in pepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients.