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Net photosynthetic rate of plants is the basis of plant production, and is an important physiological index to reflect the growth of plants. In this paper, hyperspectral reflectance of flag leaves of wheat was transformed with the first derivative and then correlated with net photosynthetic rate(Pn) to determine the sensitive bands, adopting three methods quadratic polynomial stepwise regression(QPSR), partial least squares regression(PLSR), back propagation neural network(BPNN) respectively to construct the inversion model of Pn for flag leaves of wheat, and to compare and analyze the prediction accuracy of the three models. The result showed that:(1) After the first derivative transformation of the original spectra of wheat leaves, and analysis with Pn in correlation the determined sensitive zone concentrate occured on 750~925 nm, and the six sensitive bands were determined as 760, 761, 767, 814, 815 nm and 889 nm.(2) Based on QPSR, PLSR and BPNN the Pn estimation model constructed was highly forecasting precision. This illustrates the three methods and sensitive band was feasible to estimate Pn. Among them the order of the ability to estimate the model was QPSR > BPNN > PLSR, which indicated the best hyperspectral model for flag leaf Pn of wheat was QPSR model whose first derivative changed after 750~925 nm reflectivity of wheat leaf.