In pulping and papermaking industry, extractives of wood chips influence the impregnation efficiency, pulp energy consumption and pulp yield. But traditional analysis methods for the content of extractives are not applicable for industrial online monitoring because of being time consuming and costly. Therefore, the present study used near infrared (NIR) spectroscopy to predict rapidly extractives content of three species of fast-growing Eucalyptus urophylla×E.grandi chips (DH32-29, DH32-26, DH33-27) grown in China’s Guangxi Province. NIR spectra of 144 fast-growing Eucalyptus were collected using a holographic grating spectrometer equipped with a halogen illumination and array detector. The benzene-alcohol extractives and 1% NaOH extractives content of 144 samples were gravimetrically determined according to the Chinese national standard test method respectively. The near-infrared spectrum were pretreated using smoothing, first derivative, second derivative, vector normalization and multivariate scattering correction in Matlab 8.0, and the models were developed for various pretreatment methods by loading PLS, LASSO, SVR and ANN algorithm. The optimal modeling methods were selected. Genetic algorithm was used to select the bands, which improved the accuracy of the models and optimized the models. In conclusion, in order to develop analysis model of benzene-alcohol extractives, smoothing, MSC and first derivative methods should be used to preprocess the original spectrum, the bands of 1 345.0~1 821.4 and 2 127.8~2 241.3 nm were selected, meanwhile, the partial least squares algorithm was used with the optimal factor 9. The model had the best accuracy for the RMSEP value as low as 0.25%, and the absolute deviation range was -0.39%~0.38%. The optimal bands between 1 345.0~1 821.4 and 2 127.8~2 241.3 nm have been associated with O—H stretching (1st overtone) of phenolic compound (1 410 and 1 447 nm), as well as C—H stretching and CC stretching group frequencies of benzene ring (2 133 nm) and other characteristic absorption. In order to establish the content analysis model of 1% NaOH, smoothing, vector normalization, first derivative should be used to pretreat the original data, the bands between 1 138.2~2 363.0 nm were picked and LASSO was adopted. The model had the best accuracy when the μ value was 12.61, the independent verification show the RMSEP value was 0.37%, and the absolute deviation range was -0.56%~0.53%. The optimal bands between 1 138.2~2 363.0 nm have been associated with C—H stretching (2nd overtone) of COCH3 (1 158 and 1 170 nm), as well as C—H stretching (1st overtone) of —CH3 (1 666, 1 681 and 1 790 nm) and other characteristic absorption. The characteristic absorption of benzene-alcohol extractives and 1% NaOH extractives on the optimal bands was analyzed from the point of view of molecular structure, and the performance of models was explained theoretically. The models can meet the actual demand and can be applied to the analysis of the content of Eucalyptus extractives in pulping and papermaking industry. The results showed that performance of near-infrared models can be developed and optimized by the selection of pretreatment and modeling methods combined with the genetic algorithm for the prediction of Eucalyptus extractives. At the same time, as an emerging algorithm, LASSO algorithm has a good ability to process co-complex linear data in near-infrared spectroscopy, and can establish models with good analysis performance.