In view of the limitations of traditional anti-drone swarm methods that focus solely on either detection or strike in a single stage, as well as the issues of missed detections and insufficient strike coverage, this paper proposes a two-stage detection-clustering anti-drone swarm strike strategy. In the detection stage, high-confidence spatiotemporal coordinates are output to provide reliable input for subsequent clustering analysis, while in the clustering stage, cluster centers are generated as key strike points, forming a closed-loop collaborative mechanism for precise detection and strike. First, the detection stage improves the YOLOv8 algorithm to address the challenges posed by the small target size and dense distribution characteristics of drone swarms. A multi-scale convolutional attention mechanism is introduced in the detection head to integrate the multi-scale features extracted by the backbone network, thereby enhancing detection accuracy. On the validation set, the mAP50 reaches 95.77% and the mAP50-95 reaches 59.66%, representing improvements of 1.2% and 2.4%, respectively, over the original model. Secondly, in the clustering stage, an improved accelerated fuzzy $C $-means (AFCM) clustering algorithm is proposed, which employs center selection and a dynamic membership reduction strategy to enhance convergence efficiency by 55% compared to the traditional FCM. Finally, tests on a self-built multi-scenario drone swarm dataset demonstrate that, at 30 fps, the full detection-clustering process takes less than 2 ms per frame, thereby validating the effectiveness of the proposed two-stage anti-drone swarm strategy and its underlying algorithms.