Abstract: Accurate and timely nitrogen status diagnosis is crucial for the nitrogen application management and yield prediction of winter rapeseed. Traditional, destructive manual measurement cannot fully meet the large-scale production in recent years, due to the tedious, time-consuming and laborious. Fortunately, the non-destructive and efficient hyperspectral technology can serve as the necessary means in modern precision agriculture. This study aims to realize the rapid and accurate diagnosis for the prediction of the nitrogen nutrition status of winter rapeseed. Data sources were collected from 90 plant samples (above-ground biomass and plant nitrogen concentration) and hyperspectral measured data at the bud stage of winter oilseed rape under different mulching and nitrogen application treatments for two consecutive years (2022-2023). Eight typical spectral indices (16 in total) were first calculated using the correlation coefficients between the original and the first-order differential (FD) spectrum and the nitrogen nutrition index (NNI). Secondly, the correlation matrix was used to extract the best spectral combination. The optimal spectral index was then selected from the correlation coefficient with NNI. Finally, the optimal spectral index was divided into three groups of input variables for the model (combination 1: the five original spectral indices; combination 2: the five first-order differential spectral indices; combination 3: the five spectral indices, with the highest correlation coefficient with NNI). Support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and back propagation neural network (BPNN) were used to construct the NNI estimation model of winter oilseed rape. Determination coefficient (R²), root mean square error (RMSE), and mean relative error (MRE) were used to evaluate the accuracy of the model. The results show that the correlation coefficient between the first-order differential spectral index and NNI was greater than that of the original, indicating a better NNI prediction using the first-order differential spectral index. There were high correlation coefficients between the spectral indices selected by the three combinations and NNI. The wavelength combinations were located in the red edge (670-760 nm), indicating the NNI variation representing the characteristic information in the red edge. The FDSAVI spectral index shared the highest correlation coefficient (0.674) with NNI. Among them, the wavelength combination was located at 712 and 678 nm. An optimal combination of the RF model and combination 2 were achieved to combine the optimal input variables of the NNI estimation model. The R² value, RMSE, and MRE on the validation set were 0.823, 0.079, and 7.513%, respectively, indicating the high accuracy of the optimal model. The finding can provide a technical basis for the potential application of remote sensing for the monitoring and diagnosis of plant nitrogen nutrition.