The heavy metals pollution in cultivated soils of main grain producing areas has become a prominent problem affecting the safety of food and living environment. The hyperspectral remote sensing technology as the frontier technology in the field of remote sensing technology, provides a new approach to access to soil heavy metal data quickly and accurately, and also provides the technical support for monitoring and predicting. Taking Xinzheng City of main grain producing areas in Henan Province as the research object, the 191 cultivated soil samples collected were divided into 144 calibration set and 47 validation set by Rank-KS method. The hyperspectral reflectance of soil samples was measured by using ASD FieldSpec 3 spectrometer in laboratory experiments. The correlation analyses between row spectral reflectance in 400~2400nm and the content of heavy metals Cr, Cd, Zn, Cu, Pb were done, and the correlation coefficient by F significance test (P=0.01) was selected which could be used to extract sensitive hyperspectral feature wavebands reflectance common to the above heavy metals as the independent variables of model. The hyperspectral inversion model was built by panel data model of fixed effect variable coefficient based on the ordinary least squares estimation method (OLS), which was about the panel data of heavy metals Cr, Cd, Zn, Cu, Pb of 144 samples in Xinzheng. The results show that the panel data model is overall significant, with high goodness of fit (R2=0.9937， F=1365.94). The result of precision test indicated that models for Cu and Pb performed well in modeling and predicting with a good ability of quantificational prediction, with relative percent deviation (RPD) values of 2.046 and 3.432 separately;Cr,Cd,Zn could perform generally in modeling and predicting with a good ability of quantificational prediction, with RPD values range of 1.4~1.8. The panel data model can be used to calculate various heavy metals at the same time and rapidly monitor soil heavy metals with hyperspectral reflectance in Xinzheng, with simple and fast calculation.