Objective: To establish a rapid method for moisture detection of high cellulose and lignin materials. Methods: The areca nut, a Chinese herbal medicine containing high cellulose and lignin, was selected. Use a near-infrared spectrometer to collect near-infrared diffuse reflectance spectra. Apply NIR Cal modeling software to preprocess the spectral data, select the optimal feature wavelengths, and use partial least squares (PLS) analysis to establish a quantitative model for areca nut moisture content. Results: A quantitative model for areca nut moisture content was acquired, with the coefficient of determination of 0.994 2, the root mean square of the calibration error (RMSEC) of 0.50, the coefficient of determination for the validation set of 0.986 7, the root mean square of the prediction error (RMSEP) of 0.68. Conclusion: This method is simple, fast, safe, practical, and accurate, suitable for the rapid determination of moisture content in materials containing high cellulose and lignin.