To address the low efficiency and long debugging cycle in modeling air combat tactics, this paper proposes an intelligent modeling method for air force tactical confrontation behaviors based on YOLO and CLIP. By integrating object detection and cross-modal semantic understanding techniques, the method achieves high-precision detection of key elements in hand-drawn tactical sketches, such as trajectory and phase number elements. It further extracts semantic relationships among these elements to automatically generate structured models of tactical behaviors. Experimental results demonstrate that the proposed method enables end-to-end tactical modeling from hand-drawn sketches. In simulation verification, the execution trajectories of both offensive and defensive sides align well with the intended tactics in the sketches. This approach provides an innovative solution for efficient transformation of expert knowledge and tactical validation.