Understanding influencers’ perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems, enabling more informed decision-making. However, this task is inherently challenging due to the unstructured, context-sensitive, and heterogeneous nature of online communication. To address these challenges, we propose a novel intelligent computational framework, Multi-domain Opinion Leader Agents Emotion Prediction (MOAEP). Our framework comprises three key components: (1) An Automatic Question Generation (AQG) module employing “Who, What, Where, When, Why, and How” (5W1H) questioning to systematically explore topic dimensions; (2) A Multi-domain Opinion Leader Agents (MOA) module that integrates enhanced Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to produce domain-specific responses; and (3) An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses, enabling proactive social computing analysis that surpasses conventional post-event methods. Experimental results demonstrate the framework’s efficacy: the AQG module generates high-fidelity outputs, while the influencer agents maintain consistent performance, achieving an average “Generative Pre-trained Transformer 4” (GPT-4) evaluation score of 6.85 (on a 0–10 scale) across multiple dimensions. In a social media conflict case study, “Russia-Ukraine War”, our framework successfully predicts key influencers’ perspectives and aligns emotional forecasts with observed real-world sentiment trends. These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.