Tea is one of the most popular beverages globally, which is greatly affected by the content of nitrogen (N) in quality. Due to the complicated and time-consuming method for determining N content in fresh tea leaves by traditional chemical analysis, this paper proposes a means for N content prediction by hyperspectral technique. The wavelet coefficients extracted by continuous wavelet transform (CWT) technology are used to estimate N content by different decomposition layers of CWT. Moreover, the predictive effects of models built by different wavelength selection algorithms are also discussed. Several 151 hyperspectral data of tea samples were collected from tea gardens in the Yingde City of Guangdong Province. The original spectra data are preprocessed by smoothing (SG), detrending (Detrending), first derivative (1st), multiple scattering correction (MSC), and standard normal variable transformation (SNV) while comparing with CWT. Then, continuous wavelet multi-scale analysis is applied to process the original spectrum for generating wavelet coefficients, and Pearson correlation analysis was also performed. Next, three kinds of methods, including successive projections algorithm (SPA), competitive adaptive weighted sampling (CARS) and variable combination population analysis (VCPA), are adopted to optimize the variable space of the spectral data after CWT transformation. At last, quantitative models of N content prediction are established and compared by PLSR with characteristic variables selected by the three above mentioned methods as input. The overall results show that the continuous wavelet analysis algorithm can improve the model's efficiency for estimating the N content of the fresh tea leaves by hyperspectral data. Furthermore, it has better performance than other conventional spectral preprocessing methods significantly. With continuous wavelet decomposition, the precision of the model for N content prediction gradually decreases with the increase of the decomposition scale.There is a good correlation between the spectrum after the continuous wavelet transforms on the scale of 1~6 and the N in fresh tea leaves,which shows that the small-scale continuous wavelet algorithm can be well applied to monitor N content in fresh tea leaves. The model established by CWT (1scale)-VCPA method has the best performance, andthe number of variables is reduced by 99.34% compared to the full band. The R² of the calibration model and prediction model respectively, are 0.95 and 0.90. Compared with the traditional spectral processing method, the accuracy is improved by 11%. It is proved that the combination of CWT-VCPA can obviously reduce the spectral dimension and improve the accuracy of the model. This research achieves an efficient way for N content prediction of tea, which provides a technical basis and reliable reference for other components evaluation of tea.