<p>Current research on emotional modeling in multi-agent systems remains relatively scarce, with most existing studies focusing on a single emotional state. This limitation hinders the ability of current models to fully capture and describe the complex and dynamic behavioral patterns exhibited by agents in diverse environments. To address this issue, this study proposes a strategy learning rule based on both emotion and personality traits. In this framework, an agent’s decision-making behavior is primarily influenced by its emotional state and personality characteristics. Emotions are generated based on the agent’s received payoff, strategy interactions, and feedback from neighboring agents. A mechanism for emotional decay is introduced to allow emotional states to evolve over time, thereby preventing irrational decisions caused by short-term emotional fluctuations. Additionally, the role of personality traits in strategy adaptation is explored. Experimental results show that agents with high compliance tend to adopt their neighbors’ strategies and form cooperative clusters, while those with high activity levels accelerate the diffusion of cooperative strategies. Comparative analysis with other learning rules further confirms the superiority of the proposed emotion- and personality-based strategy learning rule.</p>