Leaf nitrogen content (LNC) is crucial for assessing plant growth status and photosynthetic capacity. Accurate LNC can aid in the rational control of nitrogen fertilizer application, which is critical for achieving efficient agricultural production. Chemical analysis methods can accurately detect nitrogen content. However, it often requires destructive sampling and cumbersome steps, which are difficult to measure in real-time. Spectral technology can enable nondestructive detection of LNC, but the high dimensionality and noise inherent in spectral data make accurate estimation challenging for precision agriculture. To achieve accurate prediction of nitrogen content in eggplant leaves, this paper proposed a feature extraction method of spectral data based on hyperspectral imaging (HSI) technology and a one-dimensional convolutional autoencoder network (CAE). The proposed method utilized pixel-level spectral data to train the CAE, fully utilizing the HSI data of leaves. This can extract deep features that retain local spectral features related to the nitrogen content distribution on the leaf surface, reducing data dimension, filtering out noise, and enhancing the accuracy and stability of the nitrogen content prediction model. In this paper, we set up four nitrogen application gradients for eggplant, obtained leaf samples with varying nitrogen content using culture, and measured their HIS data. Multiple scattering correction algorithm was used for data preprocessing. The HSI-CAE method, competitive adaptive reweighting (CARS) algorithm, and random frog (RF) algorithm were used to extract spectral datas deep features and characteristic wavelengths, respectively. The partial least squares regression(PLSR)models were built based on these features. The influence of deep features and different feature wavelength combinations on the accuracy of the prediction model was compared to determine the optimal feature extraction method. The results were as follows: the test set determination coefficient of the prediction model, established by using deep features from the CAE encoder of different depths, was greater than 0.85. When 28-dimensional features were output, the test set determination coefficient was 0.910 2, and the root mean square error was 3.118 9 mg·g-1. It was found that the CAE-PLSR model has the best prediction performance, which verified the feasibility and superiority of the HSI-CAE feature extraction method for estimating nitrogen content in eggplant leaves. In conclusion, the HSI-CAE feature extraction method can efficiently analyze HSI data and extract its deep features. These features contain information highly related to nitrogen content. The deep feature modeling used in this research greatly reduced the complexity of the model. It effectively improved the accuracy of the nitrogen content prediction model, providing a new way of implementing accurate prediction of nitrogen content based on the HSI technology.