Spectral data transformation and feature wavelength extraction are two important spectral pretreatment methods, which play an important role in eliminating environmental interference. Previous literature mainly compared different spectral data transformation methods and there was less studyon the spectral feature wavelength extraction methods and the combination of the two methods. In order to obtain suitable spectral pretreatment method and improve the accuracy of LSSVM model of sediment nitrogen in the intertidal zone, the effect of 4 spectral transformation methods combined with 3 characteristic wavelength extraction methods on the accuracy of LSSVM model of sediment nitrogen is studied for accurate prediction of sediment nitrogen in the intertidal zone. The results showed that the spectral transformation methods of multivariate scattering correction (MSC) or normal distribution (SVN) increasedthe correlation between spectra and nitrogen content and the highest correlation reached 0.69 and 0.71 respectively. MSC and SVN improved the prediction accuracy of LSSVM model, and the prediction R2 and RPD are 0.88, 0.87 and 2.78, 2.69, respectively. The feature wavelength extraction method of uninformative variable elimination (UVE) also improved the prediction accuracy of LSSVM model, model test R2 and RPD were 0.89 and 2.70, respectively. However, not all of the characteristic wavelengths extracted by UVE have a high correlation with nitrogen content. In addition, the combination of UVE and MSC or SVN also improved the prediction accuracy of the model, but it is not better than UVE alone or MSC or SVN alone. The results of this paper can provide a technical reference for nitrogen estimation and spectral data preprocessing of intertidal sediments.