A fast burst point area identification and segmentation algorithm based on YOLACT is proposed to capture the blast point in artillery countermeasure training system. Firstly, the feature extraction network structure and parameters are modified for the target of the blast point area. The prediction branch network and the mask generation network are combined to output the location and boundary area of the blast point. Finally, the location of the blast point is calculated according to the boundary information. The experimental results show that the method in this paper can accurately identify and segment the target of the blast point on the constructed blast point data set, and the speed reaches 21.2 fps, which is better than the comparison algorithm as a whole, and can solve a basic problem in the artillery confrontation training system.