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Near infrared spectroscopy (NIRS) is an indirect analytical technique, and its application needs to establish corresponding correction models. In order to improve the explanatory power of the model Power, prediction accuracy and modeling efficiency require wavelength selection of NIRS to optimize and minimize redundant information. Five intelligent optimization algorithms for smoke control: group optimization (ACO), genetic optimization (GA), particle swarm optimization (PSO), random frog (RF) and simulated annealing (SA)Leaf total nitrogen and nicotine near infrared spectroscopy data were selected as characteristic wavelengths. Combined with partial least squares (PLS) algorithm, several tobacco leaf total nitrogen and nicotine were constructed.