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Taking industrial and mining reclamation land as the research object, based on the ASD FieldSpec 4 hyperspectral remote sensing data, combined with the field survey data of soil heavy metal attributes, using regression analysis and feature selection methods, the retrieval research and experiment of soil heavy metal content based on hyperspectral data were carried out, and the selection and comparison of empirical models were conducted. The correlation between soil heavy metal concentration and spectral data was effectively improved by the first derivative and logarithmic reciprocal etc. On this basis, three regression analysis models, including partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were used to carry out the inversion experiment of heavy metal content in soil. The results showed that the partial least squares regression (PLSR) had the highest precision for the retrieval of heavy metal concentration in the reclaimed soil, especially for the cadmium (Cd) concentration, which was the main obstacle factor in the area. The determination coefficient (R2) of fit for the set was 0.76. Particle swarm optimization (PSO), genetic algorithm (GA) and Relief F were used to optimize the partial least squares regression (PLSR) model. The results indicated that PSO can effectively reduce the dimension of characteristic band variables and further improve the model inversion. And the R2 of fit was increased from 0.76 to 0.84. In conclusion, based on hyperspectral data, the combination of partial least squares regression (PLSR) and particle swarm optimization (PSO) can effectively measure the concentration of heavy metals in the soil of industrial and mining reclamation area, and it can provide theoretical methods and technical support for the detection of land quality and ecological indicators in the reclamation area.