In the interactive structure of social media platforms, the comment section has become the primary space for users to express their opinions. However, the proliferation of Internet troll comments has severely disrupted platform order and hindered the user experience. Existing governance methods mostly rely on manual intervention and traditional approaches, such as banning and deleting content. Still, these methods are inadequate in addressing troll comments’ large volume and complexity. This paper introduces an automatic response generation framework based on Large Language Models (LLMs), referred to as Human Feedback Based Response to Internet Trolls (TRHF), to address this issue. This framework utilizes multi-module semantic understanding, fine-grained classification, expert model-driven prompt design, and a reward constraint mechanism to generate responses that neutralize troll comments automatically. The framework’s performance is comprehensively evaluated using the Rational-Toxicity-Variety (RTV) metric, which assesses response effectiveness from rationality, toxicity suppression, and response diversity perspectives. Experimental results demonstrate that the TRHF framework enhances the effectiveness of intervention in troll comments and offers high real-time performance and ethical adaptability, providing a viable technological solution for intelligent governance on social media platforms.