Near infrared spectroscopy analysis as a reliable, rapid, little sample preparation requirement, low-cost, convenient, nondestructive and green technique becomes more and more important in the area of soil nutrition measurement. Near infrared spectroscopy are highly sensitive to C—H, O—H and N—H bonds of soil components such as total nitrogen (TN) making their use in the agricultural and environmental sciences particularly appropriate. The analytical abilities of near infrared spectroscopy depend on the repetitive and broad absorption of light by C—H, O—H and N—H bonds. A total of 243 soil samples were collected from wencheng, Zhejiang province. Raw spectra and wavelength-reduced spectra with 3 different pretreatment methods (Savitzky-Golay smoothing (SG), Reduce (RD), and Wavelet Transform (WT)) were compared to determine the optimal wavelength range and pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Projections Algorithm (SPA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with Wavelet Transform (WT) preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) and LS-SVM were applied to build models with the selected wavelength variables. The overall results showed that PLS and LS-SVM models performed better than ELM models, and the LS-SVM models with the selected wavelengths based on SPA obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.63, 0.007 9 and 1.58 for prediction set. The results indicated that it was feasible to use portable short wave near-infrared spectral technology to predict soil total nitrogen and wavelengths selection could be very useful to reduce redundancy of spectra.