Abstract: Coal-rock interface recognition technology is one of the key technologies for intelligent mining in coal mines. Based on high-frequency radar wave detection technology, high-precision detection of coal-rock interfaces can be achieved with mining, but there are still safety risks for equipment caused by rib spalling and roof caving in ultra-high mining heights (≥6 m) in mines, as well as spatial restrictions on equipment passing through during sudden changes in mining height (mining height ≤2 m). Based on previous work, this paper proposes a rapid dynamic detection system and method for coal-rock interfaces in mines using nodal GPR. The main contents include: ① Explaining the principle of the nodal GPR observation system in mines, designing a coal-rock interface recognition observation system plan and radar antenna sensor installation method based on the actual environment of the mine working face; ② Studying and proposing a nodal acquisition control system and information interaction transmission design plan to achieve dynamic data acquisition, control, and storage; ③ Studying and proposing enhanced processing methods for sensor detection data and coal-rock interface recognition algorithms based on nodal acquisition methods and radar reflection echo characteristics of coal-rock interfaces, which can effectively achieve intelligent recognition and tracking of coal-rock interfaces, as well as calculation of coal seam thickness and spatial coordinates. To verify the feasibility of this method, multiple geological radar antenna sensors with a center frequency of 1.5 GHz were used for physical model verification experiments, and a comparative analysis of nodal data acquisition and continuous data acquisition results was conducted. The experimental results show that both nodal and continuous acquisition methods can effectively identify coal-rock interfaces. Compared with continuous acquisition methods, the nodal detection method proposed in this paper can achieve rapid and dynamic repetitive data acquisition, with a single acquisition time controlled within 10 seconds, an average error of 1.07 cm for coal seam thickness detection results, a maximum error of 1.47 cm, and an average error percentage of 7.64%. This method provides technical support for dynamic high-precision detection of coal-rock interfaces in intelligent mining in mines.