Abstract: With the rapid development of large language models,role-playing intelligent agents can simulate specific characters’ linguistic styles and personality traits,which provide more natural and immersive human-computer interaction experiences.To enhance the role-playing consistency and empathetic quality of model responses,researchers have introduced retrieval-augmented generation mechanisms that integrate external knowledge to support personalized generation.However,existing methods often rely on fixed weighting schemes,making it difficult to balance semantic and emotional factors according to conversational contexts.To address this limitation,this paper proposes an emotion-aware retrieval-augmented generation model for role-playing language agents.The proposed model adopts a three-stage architecture consisting of representation,retrieval,and generation.In the representation stage,semantic-emotional embeddings are constructed by integrating large language models with Plutchik’s Wheel of Emotions,forming a unified representation of semantic content and emotional states.In the retrieval stage,the traditional cosine similarity is improved and extended with an emotion-semantic fusion strategy to compute a comprehensive similarity score,and two optimization indicators are further designed to refine the fusion process,enabling a flexible fusion between semantic relevance and emotional alignment.In the generation stage,retrieved role-related documents are combined with user inputs to guide the language model in producing responses that are semantically coherent,emotionally consistent,and aligned with the intended character’s personality.Experimental results on the CharacterEval public dataset show that the proposed model outperforms baseline approaches in terms of accuracy,mean squared error.

Key words: Large language models, Role-playing agents, Retrieval-augmented generation, Sentiment analysis