Wheat is one of the most important food crops. The content of wet gluten in wheat is a key factor to determine the degree of flour gluten. Near infrared spectroscopy was used to predict wet gluten content of wheat by establishing prediction model in this paper. The authors did some processing on the identification of abnormal samples, pretreat spectral data and partitioned calibration set to improve the predictive ability of the model, especially made a deep research on the characteristic spectral bands. The authors collected 100 spectral points and divided them into 25 groups. Through eliminating a set of spectral points to create the partial least-squares regression model and retaining the spectral combinations which had better predictive ability to continue filtering, the authors got the characteristic spectral bands. The result showed that r, R2, RPD and SEP of the model created by the whole spectral data reached 0.923, 0.848, 2.564 and 1.421 respectively, while the results of model created by the characteristic spectrum were 0.950, 0.901, 3.177 and 1.149 respectively. The predicting ability of the latter obviously improved.