Achieving prompt hemostasis after femoral artery access is critical for patient comfort and complication reduction in percutaneous coronary interventions. Most of the existing hemostasis methods either rely on manual control or are difficult to adapt to personality feelings. In this paper, we introduce an advanced framework for hemostatic robot control based on Reinforcement Learning from Human Feedback (RLHF). A Reinforcement Learning (RL) algorithm is developed to learn a control strategy to guide the robot to adaptively adjust direction based on real-time pressure sensor data and accurately track the puncture site for effective hemostasis. Virtual environment simulations demonstrate the algorithm’s efficiency and accuracy, surpassing traditional reinforcement learning methods in convergence speed and performance, highlighting the potential of human feedback to humanize robotic operations.