Due to the needs of industrial development, the different content and uncertain distribution of magnesite mineral lead to great difficulties in o determining its grade, therefore, we propose a combination of near-infrared spectroscopy and the ELM magnesite grade classification model. The model can achieve rapid classification of magnesite grade. Near infrared spectroscopy, considering that different types of H group in magnesite have different absorption degrees to near-infrared spectroscopy, is used to determine the composition and content of magnesite. It is simple, fast, accurate and efficient without destroying the sample. In this paper, we take magnesite 30 group from Yingkou City, Liaoning Province Dashiqiao for the study, collecting their magnesite NIR data samples at 30×973, using principal component analysis (PCA) for data dimensionality reduction process. The main element contribution rate is greater than 99.99% obtained characteristic variables of 10, established quantitative analysis ELM algorithm mathematical model, take 20 groups of samples as the training samples (including 6 super group, 14 groups non), 10 groups of samples for testing samples (including super grade4 groups, 6 groups non), ELM algorithm model hidden layer nodes selection 20. In order to further improve the classification performance, two kinds improved ELM algorithm models are proposed: conduct optimization selection ELM for the traditional ELM input weights and threshold using the circulation patterns and integrate integration-Featured ELM based on Featured ELM. And compare to which use the artificial method, chemical method and BP neural network model approach. The results showed that magnesite grade classification with the near-infrared spectroscopy and ELM model have a distinct advantage regardless of cost or time, and the accuracy rate can reach over 90%, which provides a new way to classify magnesite grade.