Abstract: The Yellow River Basin is an energy basin that has the dual responsibility of ecological environment governance and economic and social development. The precise and intelligent recognition of the categories, numbers and characteristics of coal-related industrial sites is a key basic issue for energy resources-low carbon development-ecological protection in the basin. This study integrated the multi-source data and deep learning algorithms to precisely analyze the characteristics of coal-based sites in 13 large-scale coal-fired power bases in the Yellow River Basin from the basin-base-site scale, obtained the high-precision and high-quality background information of coal-power bases, and proposed a new method of real-time real-scene intelligent recognition of spatial characteristics of coal-related industries. In this study, ① Multi-source data such as Google image, GF-6 image, Sentinel-2 image, etc. were collected as coal-based site samples from 13 large-scale coal-fired power bases to build four datasets of coal mine sites (open-pit), coal mine sites (underground), coal-power sites, and coal chemical sites, covering 21 categories of samples. According to each type of sample, 6×10 samples were set for each hexagonal cell, totaling 1260 site samples. The confidence interval of the optimal sample number-highest recognition efficiency-optimal recognition model was 80%−86%. ② A coal-based site classification quantitative model (CSCQM) and a coal-based site range characteristic model (CSRCM) were established. The average accuracy of the models was 0.837. The background information of coal-related industrial sites in the Yellow River Basin were clarified, and a high-precision site intelligent recognition method based on Google image base map overlaying site intelligent recognition model calculation results was proposed. ③ The precise background data of the Shendong coal-power industrial agglomeration area in the basin were analyzed. Analyzed by remote sensing based ecological index (RSEI), the surface ecological quality of the 2 km core area of coal-based sites was significantly affected by coal mine and coal-power industries, while the 5 km buffer zone was not significantly affected, and the 8 km control zone was basically not affected by coal mine and coal power industries. Thus, the low-carbon pathways such as dynamic remediation and key management by region and stage were proposed. ④ The precise background data of the Ningdong coal-power-chemical industrial agglomeration area in the basin were analyzed. In 2022, the area of coal mine sites covered an area of 17.81 km², accounting for 34.1% of the total area, the area of coal chemical sites covered an area of 22.3 km², accounting for 42.6% of the total area, and the area of coal-power sites covered an area of 12.2 km², accounting for 23.3% of the total area. The area ratio was coal chemical sites > coal mine sites > coal-power sites. Then, using the PSR (Pressure-State-Response) model, the comprehensive score of risk management was obtained as 53.93 points, which was 27.2% higher than that in 2003. A zoning management mode of ecological maintenance zone, production monitoring and early warning zone, damage repair and reconstruction zone, and other regulation zone were implemented. The study provided some technical methods and practical support for the potential pollution control, site management and regional ecological restoration of coal-related industrial sites.