Iron ore grade is an important index used to evaluate the degree of wealth and economic value of iron ore, and the verification efficiency of iron ore grade greatly influences the efficiency of iron ore mining. Because of the advantages of hyperspectral images in the fields of substance classification and content inversion, such as fast analysis speed, high accuracy, and non-destructive, this study collected hyperspectral images of Anshan-type iron ore in the two bands of VIS-SWIR and NIR, respectively, and discussed the feasibility of realizing grade inversion of Anshan type iron ore based on hyperspectral images. First, the average spectral representation in the ROI of the hyperspectral image is extracted, and the spectral data of the corresponding samples are transformed by multivariate scattering correction (MSC) and Standard normal variate transformation (SNV), respectively. Then, Monte Carlo uninformative variable elimination (MCUVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) were used to extract the characteristic bands of the spectral data before and after the transformation. Finally, the quantitative inversion model of Anshan type iron ore’s iron grade is established using radial basis function neural network (RBFNN) and extreme learning machine (ELM). The results show that after the MSC transformation of spectral data in the VIS-SWIR range, the ELM grade inversion model established by using the feature bands extracted by CARS has the best effect (R²=0.90, RPD=3.02, RMSE=3.27, MAE=2.77). Applying the MSC-CARS-ELM model to the VIS-SWIR hyperspectral image of an Anshan-type iron ore sample can generate a pixel-level iron ore grade distribution map. This study provides a new method for realizing grade inversion and visualization of Anshan-type iron ore quickly and effectively, which has important application value in geology and mining.