To meet the current demand in China for Eucalyptus globulus and Acacia mangium mixed pulping, a study was conducted to collect the near infrared (NIR) spectra of 150 mixed samples of E. globulus and A. mangium in which the content of E. globulus was manually controlled. After the original spectra were pretreated by first derivative and standard normal variate (SNV), the least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to calculate the optimal adjustment parameters of 14.30, 19.16, 12.10, and 9.74, respectively. The optimal calibration models for the content of E. globulus, holocellulose, pentosan, and acid insoluble lignin were generated. An independent verification of the calibration models showed that the root mean square error of prediction (RMSEP) for these models was 1.59%, 0.54%, 0.66%, and 0.40%, respectively. The absolute deviation (AD) was -2.58% to 2.73%, -0.91% to 0.84%, -1.19% to 1.06%, and -0.61% to 0.64%, respectively. The prediction performance of the four models was sufficient for real-time analysis in the pulping production line. The LASSO algorithm was judged to be efficient for the prediction and analysis of mixed raw materials in pulping industry.