To solve the problem in prediction of soil heavy metal content at trace levels by hyperspectral data and improve the accuracy of prediction in soil chromium (Cr) content, fractional order differential algorithm was brought in to preprocess hyperspectral data. With 168 samples of soil taken from the open coalmine area in Eastern Junggar Basin, China, the soil heavy metal Cr contents and the reflectance of these samples were measured by indoors experiments. The hyperspectral data were preprocessed by using fractional order differential algorithm, all of the wavelengths among 401~2400nm were used to calibrate the hyperspectral estimation models of soil Cr content by partial least squares regression (PLSR) and the predicted values were used in visualization analysis. Finally, the possibility of prediction of chromium content in soil with hyperspectral data preprocessed by fractional differential in coalmine area was discussed. The results showed that fractional order differential model of the raw reflectance and the absorption rate transform both achieved the best performance at the 1.8-order derivative. Among all of the models through fractional order differential preprocessing, the model based on 1.8-order derivative of absorbance transform (RMSEC was 7.68mg/kg, R2c=0.83, RMSEP was 8.39mg/kg, R2p=0.78,RPD was 2.14) was much better than others, and had better performance in predicting Cr content in desert soil. Then the spatial distribution of the actual Cr content and its estimation values in soil of the study area were obtained by inverse distance weighted (IDW) algorithm. Moreover, the spatial distributions showed the same trend. The results showed that quantitative inversion of soil Cr content and the spatial distribution of large scale were feasible by this method. This research would provide scientific basis and technical support for the application in monitoring heavy metal contamination by hyperspectral data.