To solve problems in common algorithms for spectral reflectance reconstruction such as the principal component analysis method producing ill-posed situation after reconstruction, we propose a spectral reflectance reconstruction method based on dimension reduction regularization polynomials. The principal component analysis method is used to conduct dimension reduction for high-dimensional spectral data of training samples. Based on the dimension reduction, the polynomial regression expansion is carried out for channel response numbers of the samples to improve the accuracy of spectral reflectance reconstruction, and Tikhonov restrictions are added to avoid ill-posed situation produced by data instability and random noise due to polynomial expansion. The results show that the precision evaluation effect of the proposed spectral reflectance reconstruction method based on dimension reduction regularization polynomials is better than that of the principal component analysis method and the polynomial regression expansion method. The proposed method can reduce the amount of spectral data, optimize the channel response, and improve the accuracy of reflectance reconstruction.