To address the challenges of heavy guidewire manipulation tasks and insufficient automation in existing robotic systems for endovascular surgeries, this paper proposes an autonomous guidewire control algo-rithm based on diffusion policy, which enhances the success rate and stability of surgical robots in per-forming such underactuated control tasks. We first models the guidewire control task as a Markov Decision Process (MDP), then employs imitation learning to train the robotic system using expert demonstration data, enabling efficient operation across various vascular environments. The expert control strategy is sub-sequently modeled as a conditional probability distribution through a denoising diffusion probabilistic model, which guides control action generation based on observed states. Simulation tests in an aortic arch environment were conducted for both left subclavian artery and brachiocephalic trunk artery branch inter-ventions, with comparative analysis against existing imitation learning methods. Experimental results demonstrate that the proposed method achieves significant performance improvements in both tasks, ex-hibits low sensitivity to hyperparameter settings, and maintains superior training stability.