Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms limits mechanistic insight. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained panic emotion dataset (namely COPE) via human-AI (Large Language Models, LLMs) collaboration, combining scalable LLM-based labeling with human annotators to ensure accuracy for panic emotion and to mitigate biases from linguistic variations. Then, we construct PsychoAgent integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 13% to 21% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque “data-driven fitting” to transparent “role-based simulation with mechanistic interpretation” for panic emotion prediction during emergencies. Our implementation is publicly available at: https://github.com/supersonic0919/PsychoAgent.