The moisture content of millet is an important indicator to measure the quality of millet. To detect millet moisture content, the two-dimensional correlation spectra of samples with different moisture contents are studied with millet moisture content as the external disturbance factor. First, obtain the near infrared spectra of 60 samples, and then, using four different preprocessing methods, create a partial least square regression (PLSR) model of sample moisture content based on the full-band spectra. Following the comparison, it is determined that the model with no preprocessing has the best effect. The calibration set coefficient of determination (R_c²) is 0.9460, root mean square error (R_MSEC) is 0.49%, prediction set coefficient of determination (R_p²) is 0.9391, and root means square error (R_MSEP) is 0.63%. Further, taking the moisture content of millet as the external disturbance factor, the spectral data of different moisture content gradients of millet is analyzed by two-dimensional correlation spectroscopy, and the wavelengths of the six autocorrelation peaks of the two-dimensional correlation synchronization spectrum are selected, and 1083, 951, 868, 1314, 1675, and 1865 nm are selected as the characteristic wavelength. Based on this, a millet moisture content prediction model is developed. The wavelength variable is reduced and the model is simplified when compared to full-spectrum data modeling. The correction set's coefficient of determination (R_c²) is 0.952, and the root mean square error (R_MSEC) is 0.60%. The prediction set's coefficient of determination (R_p²) is 0.897, and the root mean square error (R_MSEP) is 0.63%. The results show that two-dimensional correlation near infrared spectroscopy can predict millet moisture content and extract the characteristic wavelength, which provides a foundation for the design of a special millet moisture detector based on discrete wavelength components.