In order to improve the identification accuracy of fresh tobacco leaf maturity, a tobacco leaf maturity identification method was proposed based on the multi-source information fusion technology of near infrared spectrum and image recognition. Using random forest method, the near infrared spectrum discriminant model, image discriminant model and multi-source information fusion discriminant model were established to detect and analyze the maturity of tobacco leaves. The accuracy rates of the near infrared spectrum model for the three flue-cured tobacco leaf maturity were 91.27%, 90.43% and 89.44%, and the accuracy rates of the image model for the three flue-cured tobacco leaf maturity were 86.20%, 86.96% and 81.23%, respectively. The accuracy of the fusion model for the identification of leaf maturity of the three flue-cured tobacco varieties was 94.08%, 94.78% and 92.96%, respectively. Compared with near infrared spectroscopy model and image model, the accuracy of fusion model is improved by 3.93% and 10.83% on average.