Accurate prediction of wheat chlorophyll content is important for guiding precision management in the field. The canopy spectrum of winter wheat canopy was measured by ASD Field Spec Handheld 2, and the first-order differential processing method was conducted on the band of 400~900nm in the paper. In order to select the sensitive bands for the chlorophyll content detection of winter wheat, the Gram-Schmidt transformation algorithm was used in the research. The insignificant variables and the redundant information were identified and removed from the independent variables set. As a result, the orthogonal transformation data of first-order differential at 848nm, 620nm and 677nm were extracted. A representative set of wheat chlorophyll content of modeling samples was selected by using sample set partitioning based on joint x-y distance algorithm (SPXY). The results showed that multiple linear regression (MLR) prediction model based on Gram-Schmidt and SPXY algorithm is better than the random sampling method. The chlorophyll content of winter wheat were clustered respectively at intervals of 0.2mg/L, 0.3mg/L and 0.5mg/L. The modeling results showed that the optimal resolution was at 0.3mg/L, the determination coefficient R2c and the R2v of the calibration model which was built based on 620nm and 677nm sensitive bands were respectively 0.730 and 0.739. The study could help to evaluate the nutritional status of winter wheat and precision fertilization.