Social media's openness fosters opinion exchange but complicates guiding users toward global consensus. Existing approaches are often invasive, modifying opinions or forcing cross-group interactions, undermining autonomy and triggering resistance. They also lack long-term planning, risking macro-level polarization despite local agreement. To address this, we propose H-NeiFi, a hierarchical, non-invasive opinion guidance framework. It models experts and non-experts separately and introduces a neighbor filtering mechanism that adaptively shapes communication without altering user opinions. Using multi-agent reinforcement learning with a long-term reward, H-NeiFi optimizes information flow while preserving interaction autonomy. Experiments show that it accelerates consensus by 22.0% to 30.7% and achieves global convergence even without experts, providing a natural and efficient paradigm for social network governance. Our code is available at: https://github.com/shijunguo44/H-NeiFi.