Nitrogen is a constituent element of amino acids, proteins, and chlorophyll in plants, which plays an important role in plant photosynthesis. UAV hyperspectral technology can estimate plant nitrogen content non-destructively and efficiently, which is significant for the timely control of tree growth and precise management. Flight height directly affects the accuracy and efficiency of plant information acquisition. In this study, UAV remote sensing images of different resolutions were acquired during the flowering stage of Carya illinoinensis (Changlin and Jiande series) by setting three flight heights (i.e., 40, 60, and 80 m). Thus, the canopy spectra of Carya illinoinensis at the corresponding heights were obtained. Raw hyperspectral data were preprocessed using the continuous wavelet transform (CWT). Furthermore, the response relationship between the LNC of Carya illinoinensis and the canopy spectrum was analyzed by combining two-band spectral indices (i.e., normalized difference spectral index, NDSI). Finally, the competitive adaptive reweighted sampling-iteratively retaining informative variables (CARS-IRIV) algorithm was used to screen the feature variables. Back propagation neural network (BPNN) and random forest (RF) algorithms were used to construct spectral response estimation models for Carya illinoinensis LNC at different heights, to reveal the impact mechanism of UAV flight heights on the canopy spectral characteristics of Carya illinoinensis and LNC. Results showed improved correlation between the canopy spectrum after CWT pretreatment and Carya illinoinensis LNC. CWT combined with NDSI performed better in improving the correlation with LNC. As the flight height increased (from 40, 60 to 80 m), the correlation with the LNC increases for both single-band and two-band spectra. The optimal LNC estimation model was CWT-scale 3-NDSI-BPNN at 40 m flight height, $R_{\text{p}}^2$=0.73, RMSEP=1.13 g·kg^-1, and RPD=1.97. The research results can provide technical support for improving the accuracy of remote sensing estimation of Carya illinoinensis LNC, and further provide a reference for researchers to use a UAV equipped with sensing devices to obtain crop information and set appropriate flight heights.