When crops are contaminated with heavy metals, their tissue structure and chlorophyll content will be destroyed, which will affect the metabolism and health of crops. People and animals will have fatal injuries if they eat the contaminated crops. Hyperspectral remote sensing is now widely used to monitor the extent of crops affected by heavy metals, and in the heavy metal pollution, the spectral crop leaves are still very similar to those of the traditional monitoring methods and spectral characteristic parameters of routine, so it is difficult to distinguish between different spectral weak information, and application of hyperspectral remote sensing is the focus and difficulty of the study. The maize leaf spectral data, chlorophyll content and relative content of heavy metals Cu2+ and Pb2+ were collected by setting different concentrations of Cu2+ and Pb2+ stress. A LD-CR-SIDSCAtan model combined with the continuum removal (CR), spectral correlation angle (SCA), spectral information divergence (SID) and tangent function (Tan) and Langmuir distance (LD) is proposed in this study, and the traditional measure, such as spectral correlation coefficient (SCC), SA (spectral angle), tangent spectrum (DSA), spectral information divergence and spectral correlation tangent (SIDSAMtan), spectral information divergence and spectral gradient tangent (SIDSGAtan) and conventional spectral characteristic parameters, such as the maximum value of red edge (MR), green peak height (GH) and red edge area surrounded by a first order differential (FAR), red edge derivative curve steepness (FCDR), blue (DB), red band depth (RD) compared to verify the feasibility and superiority of the model. The LD-CR-SIDSCAtan model was applied to measure the spectral difference information about the overall waveform and the subband of maize leaves under Cu2+ and Pb2+ stress at different concentrations. The results show that the LD-CR-SIDSCAtan model realized the qualitative analysis of heavy metal Cu2+ and Pb2+ pollution, could measure the spectral correlation coefficient of more than 0.99 of the difference information between the similar spectral information, and waveform difference information was significantly related to the leaf chlorophyll content and the relative content of heavy metals Cu2+ and Pb2+ that measured, and also found the spectra response wave band under the stress of heavy metals Cu2+ and Pb2+. When the whole spectral range of spectral data is measured, the spectral difference is more obvious when the value of the model is negative. When the value of the model is positive, the larger the value of the model is, the larger the spectral difference will be. Therefore, with the increase of heavy metals Cu2+ and Pb2+ concentration, the difference of spectra increased, which means that the heavy metal Cu2+ and Pb2+ pollution degree is more serious; maize plants suffer from heavy metal pollution in Cu2+ stress when measuring the local subband range of spectral data, in the “blue” and “red edge”, “near the Valley”, “at the peak of B” were specially sensitive to heavy metal Cu2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Cu2+; when the maize plants are under heavy metal pollution in Pb2+ stress, in the “Purple Valley”, “blue”, “yellow” and “red Valley”, “red edge”, “near at the peak of A” were specially sensitive to heavy metal Pb2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Pb2+. Finally, through the linear fitting analysis of the application results of LD-CR-SIDSCAtan model and the content of Cu2+ and Pb2+ in maize leaves, the pollution degree of heavy metals Cu2+ and Pb2+ to maize plants was inversed and predicted.