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In NIR spectral modeling, the method of screening wavelengths with consistent stable signals (SWCSS) is based on a standard-free algorithm. However, the wavelengths selected by SWCSS may contain invalid information. In this paper, the Competitive Adaptive Reweighted Sampling (CARS) wavelength optimization algorithm was used in conjunction with SWCSS to eliminate the uninformative variables in the wavelengths selected by SWCSS. The SWCSS-CARS method was based on three near-infrared spectrometers (Lengguang 1, Lengguang 2, and Lengguang 3), with Lengguang 1 as the master and the other two instruments as the targets, using a total of 84 sample spectra of five types of pulpwood and their lignin contents as the research objects. Compared with the full spectrum, the number of wavelengths was reduced from 1601 to 24 in the model built using the coupling algorithm. For target 1, the value of RPD was improved from 1.9247 to 3.1880; for target 2, t the value of RPD was improved from 1.7415 to 3.2508. The wavelengths selected by the SWCSS-CARS coupling algorithm were able to build stable, robust models. [ABSTRACT FROM AUTHOR]