In this study, a big research progress has made in the research concerning leaf nitrogen content (LNC) nutritional spectral diagnosis on winter wheat at several growth stages, in which typical wave bands were put forward and quantitative models were constructed and validated. First, the unmanned aerial vehicle (UAV)-based hyperspectral data and the corresponding LNC data on winter wheat at several growth stages were obtained through experimenting in 2015, and the measured hyperspectral data and the LNC data were also obtained from the field-measured experimentation in 2014. Second, the spectral indices were calculated using UAV-based hyperspectral data and measured hyperspectral data, and the statistical regression models for diagnosing the LNC of different growth stages were constructed and analysed. Then, the correlation between the LNC and the spectral band is analysed. A method for selecting the typical bands of hyperspectral data responding to the LNC is proposed using spectral correlation as the basis. The UAV-based hyperspectral bands sensitive to the LNC of winter wheat are determined using this method. Finally, the hyperspectral quantitative models for diagnosing the LNC at the four stages are established by multifactor statistical regression and Back Propagation (BP) neural network methods. By comparing the modelling and verifying the coefficient, the UAV-based quantitative hyperspectral models’ effectiveness and practicability are then validated. The modelling results show that the predicted values are very ideal in jointing stage, flagging leaf stage, and flowering stage, while it is slightly less in the filling stage. The BP neural network modelling results were generally better than the multiple linear regression modelling results. This demonstrates that the effectiveness and spectrum sampling precision of UAV-based hyperspectral data are believable.