Abstract: As a temporary storage facility in the coal mining and transportation process, coal bunkers are prone to safety accidents such as collapse and breach if their conditions are not properly monitored during long-term use. Due to the working environment of coal bunkers, traditional monitoring methods for coal bunkers have problems such as difficult perception and poor accuracy. A coal bunker condition monitoring method based on 4D millimeter-wave radar is proposed. By installing 4D millimeter-wave radar on the top of the coal bunker, it scans the velocity point cloud inside the coal bunker from top to bottom. The static point cloud and the moving point cloud are separated according to the Doppler velocity. For the separated dynamic point cloud, the velocity of the dynamic point cloud is calculated to obtain the coal feeding state of the coal bunker. For the separated static point cloud, calculate the farthest point of the static point cloud to obtain the remaining coal stacking height of the coal bunker. Then, use the Alpha shape algorithm to envelope the separated static point cloud to obtain the internal fitting contour of the coal bunker, realize the visualization of the coal stacking state of the coal bunker, and finally calculate the distance from the contour point cloud to the center of the coal bunker under different coal bunker heights. The contour curves composed of the minimum distances from the coal bunker contour to the center point of the coal bunker at different heights were obtained. The contour curves composed of the minimum distances from the coal bunker at different heights were transformed into image features by using Gram angular field transformation. The improved Dense Net neural network model was utilized for machine learning to achieve intelligent recognition of the wall-hanging state of the coal bunker. At the same time, a coal bunker monitoring system was developed based on Unity and applied in industry. The results show that the coal bunker condition monitoring method based on 4D millimeter-wave radar has a good effect and can meet the synchronous monitoring of the coal pile height, coal feeding status and coal bunker wall connection status of the coal bunker. Compared with lidar, the maximum accuracy deviation of 4D millimeter-wave radar point cloud in perceiving the coal bunker contour is 0.22 m. This technology provides a new method for intelligent perception of coal bunkers.