Spectral reflectance of crop will be changed slightly when crop is stressed by heavy metal. The changes of crop spectral reflectance have considerable significance for crop contamination diagnosis. However, vegetation photosynthetic components are complex, which means that there may be no visible symptoms in leaf spectral reflectance when the crop is stressed by heavy metal. And therefore the object was to develop a weak information extraction method to excavate the vegetative stress signals through minimizing the effects of background materials, such as those caused by nonphotosynthetic components. A VMD-MSE model was built to excavate and measure the weak information in corn leaves spectrum by introducing the variational mode decomposition (VMD) into hyperspectral weak information detection and combining with multiscale entropy (MSE). The model value could be obtained after treating corn leaves spectrum by VMD-MSE model. In addition, linear regression models between model values of corn leaves spectrum under different stress concentrations and Cu2+ contents in corn leaves were established. The results showed that the spectrum singular features of the original spectrum of corn leaves can be extracted effectively after three times decomposition of variational mode decomposition. Model values of five scales were obtained by calculating the multiscale entropy of the result of threetime variational mode decomposition. And VM, the model value at five scales, had a significant negative correlation with Cu2+ contents in corn leaves, and the most significant correlation was between the firstscale model value (VM1) and Cu2+ contents in leaves. The linear regression model established based on VM1 and Cu2+ contents in corn leaves was proved to be optimal by comparing the application results of five Cu2+ contents prediction models. Therefore, the VMD-MSE model can provide a new method for pollution information extraction, crop contamination diagnosis and Cu2+ contents prediction.