摘要： Dynamic spectrum (DS) is expected to achieve non-invasive analysis of blood components by extracting the absorbance of arterial blood at multiple wavelengths. However, the nonlinearity caused by scattering of blood components is still a factor that limits the detection accuracy. According to the idea of “overlay modeling” in the “M + N” theory, theoretically, the consideration of nonlinear factor in modeling analysis can further improve the prediction accuracy of calibration model. But there is currently no recognized formula to describe this nonlinear relationship. In this paper, the ability of RBF neural network to approximate arbitrary nonlinear functions with arbitrary precision is used to approximate the nonlinear relationship between the spectrum and the component concentration from the perspective of ?tting. The calibration sets and prediction sets were randomly selected from the VIS & NIR band DS data of 231 volunteers, and 10 groups of modeling experiments were carried out. The results showed that compared with the conventional partial least squares (PLS) modeling method, the modeling indicators (correlation coe?cient (R) and root mean square error(RMSE)) of prediction set using radial basis function (RBF) neural network modeling have been signi?cantly improved. The modeling experiments suggest that the nonlinearity caused by scattering should not be ignored in DS non-invasive blood component analysis. By ?tting the nonlinear relationship, RBF neural network can better re?ect the actual mapping relationship between the spectrum and the component concentration, because it can not only consider the general part (Linear factor) in DS, but also the details (Nonlinear factor), which can e?ectively improve the accuracy of the non-invasive blood component analysis based on DS.