A large number of coal mines are widely distributed over China. Bulk coal gangue deposits seriously affect the mining area environment, and some mishandling of coal gangue may cause spontaneous combustion and explosion, which poses a direct threat to mine safety. The comprehensive utilization of coal gangue can effectively alleviate this problem, and it is of significance to the ecological safety and sustainable development of mine. Depending on the burning state, coal gangue is divided into two types - burned and unburned gangue, whose hidden dangers of security and harm to the environment are different, as well as ways of comprehensive utilization. Therefore, it is very important to do the classification, recognition and monitoring of the coal gangue. The current monitoring methods are mainly the field investigation with low efficiency and high cost, almost impossible for meeting the actual demand of coal gangue monitoring.Tiefa mine in Liaoning Province was chosen as the study area. Firstly, a total of 106 typical coal gangue samples were collected from waste dump in mining areas. Then, SVC HR1024 spectrometer was used to test the visible and near infrared spectrum of samples, and a differential spectral index NDGI was constructed to identify the burned and unburned gangue based on the difference of spectral characteristics of the burned and unburned gangue. Finally, the laboratory spectral data and the corresponding satellite remote sensing images were utilized for verifying the index. The random forest classification method was used as a contrast to the results of the laboratory spectrum treatment. The results showed that the slope of the spectral curves of burned gangue samples was higher ranging from 350 to 750 nm, and the reflectance within range of 550~630 nm increased sharply, while the slope of the unburned gangue in the whole visible bands of spectrum was lower. The threshold of the NDGI index was set as 0.25 to distinguish the burned and unburned gangue. The laboratory spectral data showed that the classification accuracy of the NDGI index is up to 99.1%, higher than that of 95.2% of the random forest classification method. The Field results showed burned and unburned areas of waste dump were distinguished and classified in Landsat8 OLI images based on the NDGI index, and the burned and unburned coal gangue areas were in good agreement with the Google Earth on the morphology and size. The overall results showed that the index can effectively distinguish the combustion states of gangue. In addition, burned and unburned gangue samples were taken for mineral identification respectively. By comparing the changes of mineral species before and after combustion, the cause of spectral difference was analyzed between the burned and unburned gangue. The results showed the oxidation from Fe2+ to Fe3+ of gangue in the process of combustion. A large increase in Fe3+ caused the formation of an obvious spectral valley characteristic at 550 nm band, and a highly reflectance appeared at 750 nm band due to the glass quality generated during combustion. The above conditions cause differences in NDGI index between the burned and unburned gangue. In this paper, the results provide a fast, efficient and accurate model and method for burned and unburned gangue distinguishment in coal mine.