In order to achieve rapid and non-destructive detection of nitrogen content in bamboo leaves, Phyllostachys aureosulcata leaves were used as samples for the hyperspectral analysis. To a certain extent, the nitrogen content in plant leaves can reflect the nitrogen condition inside the plant, which has a good prediction effect on plant growth. The spectral data of bamboo leaf was obtained by using the field portable terrain spectrometer with spectral range from 350nm to 2500nm. Correlational analysis was conducted between the nitrogen content measured by the chemical method and the hyperspectral reflectance, the first order differential reflectance, the logarithmic first order differential reflectance and the second order differential reflectance of bamboo leaves, respectively, and the characteristic bands were obtained. Four estimation models of nitrogen content of bamboo leaf were established by the binary linear regression, multivariate stepwise regression, partial least squares regression (PLRS) and principal component analysis-BP neural network regression (PCA-BP). respectively. The experimental results of four estimation models showed that by using the logarithmic first order difference of the hyperspectral reflectance, PCA-BP estimation model with 6-10-1 topology based on principal component analysis had better estimation result. The determination coefficient (R2) and root mean square error (RMSE) were 0.838 and 0.0452, respectively.