At present, heavy metal pollution in the soil is becoming more and more serious in China. Hyperspectral remote sensing has become a hot spot in the research of heavy metal pollution in crops by reason of its characteristics such as high spectral resolution and integrated maps with spectral. The spectral of crops will change slightly after being contaminated by heavy metals, how to explore the sensitive bands in the leaves spectral stresses by heavy metal pollution is a current research direction. In this study, a new copper stress vegetation index (NCSVI) was proposed to explore the sensitive range of corn leaves spectral under copper stress. By designing corn stress experiments with different gradients, the spectral and the contents of Cu²⁺ in corn leaves under each copper stress concentration were determined. First, the spectral of corn leaves were divided into 11 sub-band intervals, NCSVI were constructed by spectral reflectance corresponded to the middle wavelength of each sub-band interval. Then, the Pearson correlation coefficient and RMSE (Root Mean Square Error) between NCSVI and the contents of Cu²⁺ in each corn leaves was calculated, combined with three conventional vegetation indexes of water band index (WBI), modified chlorophyll absorption ratio index (MCARI) and normalized water index (NDWI). Finally, the corn leaves spectral which obtained under the same experimental conditions in other year were selected for verification to confirm the stability and effectiveness of NCSVI. The results show that among the 11 sub-band intervals, only the four sub-band intervals of a green peak, red edge, near the valley, and near peak A, the absolute value of the correlation coefficient between NCSVI and Cu²⁺ contents of corn leaves were higher than 0.9, respectively to -0.94, -0.97, -0.94, -0.96, as for RMSE, the root mean square error were less than 15, reached to 12.57, 8.71, 12.71 and 10.06. However, the highest correlation coefficient of WBI, MCARI and NDWI only reached to 0.75. The smallest RMSE was 24.21. Indicating that NCSVI corresponded to the four subintervals had a better indicator of copper pollution in corn leaves. The above results were verified by corn experiments under the same conditions in a different year, and it was found that among the 11 subintervals, only four subintervals of a green peak, red edge, near the valley, and near peak A had its absolute value of the coefficient R between NCSVI and the contents of Cu²⁺ in corn leaves were greater than 0.9, respectively to -0.9, -0.97, -0.97 and -0.93, as for RMSE, the root mean square error were less than 1.55, reached to 1.50, 0.85, 0.78 and 1.29, which were higher than WBI, MCARI and NDWI, and with the same sensitive sub-band intervals in the experiment of 2016, indicating that NCSVI could detect the sensitive range of corn leaves spectral stressed by Cu²⁺, with the characteristics of high efficiency and good stability. The NCSVI index proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical supports for the research of heavy metal pollution in other crops.