Leaf C/N ratio is an important indicator reflecting the individual nutrient utilization efficiency of Camellia oleifera. Estimating C/N ratio based on canopy hyperspectral characteristics can provide important theoretical basis for nutrient monitoring and precise fertilization of Camellia oleifera. There are very limited studies on non-timber product forests physical and chemical properties -using hyperspectral data, especially for Camellia oleifera with the synchronous biological characteristics of flowers and fruits. In addition to the collinearity problem, its complex physical and chemical properties pose great challenges to the response of sensitive spectral characteristics and the construction of estimation models. In this study, the Changlin series of Camellia oleifera in the Huanagshan area of Anhui Province was taken as the research objects. The canopy spectra of 120 Camellia oleifera plants were collected in the field, and the hyperspectral characteristics of the 400~1 000 nm wavelength range in the visible and near-infrared spectral regions were selected for analysis. Original hyperspectral data were processed by using multiplicative scatter corrections (MSC) and first derivative (FD) transformations, and three types of two-band indices (i.e., difference index-DI, ratio index-RI, and normalized difference index-NDI) were constructed respectively. Correlation analysis was used to observe the changes inspectral response feature regions under different processing methods. Response variables were extracted by variable combination population analysis (VCPA), and an optimal feature variable subset was obtained by removing collinearity to construct three machine learning models (i.e., random forest-RF, support vector machine-SVM and BP neural network-BPNN). Finally, the effects of spectral parameters on model estimation accuracy under different treatments were compared, and the optimal estimation model of the C/N ratio of Camellia oleifera leaves was identified according to model evaluation indices. Results showed that: (1) The original spectrum after MSC or FD feature transformation combined with VCPA can mine more potential variables. (2) The combination of a two-band spectral index expands the response region of sensitive bands and further enhances the ability of VCPA to select characteristic variables. FD-RI and FD-NDI are with the best treatment effect. (3) The overall accuracy of the three machine learning models ranked indescending order were BPNN>RF>SVM. Among all models, the BPNN model constructed by FD-NDI spectral parameters has the best prediction ability performance. The determination coefficient (R2) of the training and test sets are 0.71 and 0.66, respectively, and the relative percent difference (RPD) is 1.74. This study established an optimal BPNN estimation model for the C/N ratio of Camellia oleifera leaves in the harvest period, which expands the application range of hyperspectral of Camellia oleifera leaves.