The detection of the wheat moisture content plays a key role in grain storage and classification. Harvested wheat grains were taken as samples in the current research. A total of 240 reaped wheat samples with different moisture contents were tested by applying terahertz (THz) spectroscopy. The frequency domain spectra and absorption coefficient spectra of wheat were obtained in the band of 0.1-1.2 THz, and the spectra were pretreated by mean centering, Savitzky-Golay (S-G), Multiplicative Scatter Correction (MSC) and Stand Normal Variate (SNV), respectively. Then a special algorithm of Tabu Search (TS) was used to find out the effective variables and remove the useless variables from the terahertz spectrum of the sample. Finally, the partial least squares (PLS) of chemometrics were used for quantitative model building and prediction. The correlation coefficient of calibration (Rc) is 0.9522. The root mean square error of calibration (RMSEC) is 0.4730. The correlation coefficient of prediction (Rp) is 0.9531. The root mean square error of prediction (RMSEP) is 0.5396. The results demonstrated that an accurate quantitative analysis of moisture in wheat samples could be achieved by terahertz time-domain spectroscopy combined with the TS algorithm. In addition, the results show that the model S-G + MSC + TS + PLS can effectively predict wheat moisture, and provide a rapid quantitative detection and analysis method for the detection of wheat moisture.