Iron ore resources are an important component of the basic industries of China’s national economy and play a pivotal role in China’s economic development. In particular, the efficiency of iron ore grade determination has a significant impact on the efficiency of iron ore mining. At present, the method of iron ore grade determination is mainly based on chemical analysis. However, it not only has the problems of high cost and long assay cycle but also cannot achieve the in-situ determination of iron ore grade, which has a lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining, so the in-situ determination of iron ore grade based on visible and near-infrared spectral analysis is an effective way to solve this problem. This paper uses visible and near-infrared spectral data and chemical analysis data from 225 test samples of Hongling Skarn-Type iron ores as data sources. First, the original data were smoothed and analyzed for visible and near-infrared spectral characteristics of Skarn-Type iron ores, and then the smoothed spectral data were processed by using two pre-processing methods, including logarithm of reciprocal and multiple scattering correction (MSC). -Before and after pre-processing, the spectral data were processed using two-dimensionality reduction algorithms, including genetic algorithm (GA) and principal component analysis (PCA), and obtain the data sources were processed by six different pre-processing combination algorithms. The PCA dimensionality reduction algorithm was used to reduce the dimensionality of the spectral data before and after the pre-processing, and the dimensionality reduced were 3, 3 and 7 dimensions respectively; The GA dimensionality reduction algorithm was used to reduce the dimensionality of the spectral data before and after the pre-processing, and the dimensionality reduced were 477, 489 and 509 dimensions respectively. Finally, based on Random Forest (RF) and Extreme Learning Machine (ELM), a quantitative inversion model of iron grades in skarn-type ores was established, and the stability, accuracy and credibility of the model were evaluated in terms of coefficient of determination (R²), root means square error (RMSE) and mean relative error (MRE). The results show that the quantitative inversion model based on the ELM algorithm, using MSC-processed and PCA-dimensioned data, is the most effective, with R² of 0.99, RMSE of 0.005 7 and MRE of 2.0%. The accuracy of the model for inversion of HongLing skarn-type iron ore grades has been significantly improved. This research provides an effective method for the real-time and rapid analysis of skarn-type iron ore grade, which is of great practical importance for efficient skarn-type iron ore mining.