Abstract: In view of a series of key technical challenges faced by the mining of super-long working face in medium-thick coal seam in China, such as complex mine pressure, instability of equipment group cooperative control, insufficient real-time performance of system perception and decision-making, and the traditional production mode of “underground operation as the main part and ground intervention as the auxiliary part” is difficult to meet the requirements of safe, efficient and less-manual mining, this study aims to break through the key technology of intelligent control of super-long working face in medium-thick coal seam, realize the leap from local automation to system intelligence, and provide a systematic solution for the construction of ten million-ton modern mine. The research adopts the method of combining theoretical analysis, technology research and development, system integration and engineering practice verification. Firstly, the adaptive support and cooperative control technology of hydraulic support is developed, and the advanced prediction method based on the support state template curve library is constructed. The multi-sensor fusion attitude sensing system is used to realize the closed-loop control, and the data-driven intelligent liquid supply dynamic coupling control method is proposed, which forms an intelligent decision support system for adaptive following under complex geological conditions. Secondly, a multi-source fusion high-precision perception and digital twin technology system is constructed, which integrates three-dimensional laser measurement, visual recognition and inertial navigation correction to realize high-precision real-time reconstruction of working face space; a multi-physical field coupling mechanism model for hydraulic system is established. Based on this, a fault early warning and diagnosis method based on virtual and real combination is studied, and a hydraulic digital twin prototype system that can adaptively match application scenarios is developed. Furthermore, the dynamic self-optimization intelligent decision-making and collaborative scheduling technology is developed, including intelligent monitoring and load balance control of coal flow state of scraper conveyor based on AI vision, intelligent planning of coal cutting of shearer, linkage control of working face and two roadway equipment and intelligent anti-collision technology of shearer. Finally, a new intelligent mining mode of “in-well decision-making-underground execution” is innovatively proposed. By constructing a remote control platform integrating ground intelligent monitoring center, big data analysis center and panoramic video stitching technology, complex data analysis and intelligent decision-making are moved up to the ground, and underground equipment is used as an autonomous execution terminal. The research results have been successfully applied in the related coal mines of Yankuang Energy (Ordos) Company Limited and Shaanxi Xiaobaodang Mining Company Limited. The engineering practice shows that after the application of this technical system, the propulsion degree of a single working face is increased by 15%−40%, the probability of hydraulic support following is not less than 95%, the number of workers in a single working face is reduced from 12 to less than 2, with a decrease of more than 83%. The efficient mining of an average of 16−18 coal mining cycles / d is realized, and the output of single-cut coal is increased by 31.25%−66.67%, creating an industry efficient record with an average speed of shearer not less than 15 m/min and a single coal mining cycle time less than 45 min. The research shows that the developed intelligent control system of super-long working face in medium-thick coal seam effectively solves the problems of collaborative control of equipment group, high-precision real-time perception and intelligent decision-making in complex mine pressure environment. The new model of “on-well decision-making-underground execution” provides a new paradigm that can be replicated and popularized for intelligent mining in the industry, which significantly improves the recovery rate, mining efficiency and intrinsic safety level of coal resources.