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<p>With the continuous advancement of artificial intelligence technology, knowledge graphs and large language models have shown great potential in the communication strategies of fashion brands. This paper presents innovative approaches that combine the two, aiming to elevate fashion brands' narrative generation and personalized communication strategies. By building a model based on the combination of knowledge graph and LLM, fashion brands can better understand consumer needs and generate more accurate brand stories, thereby improving market responsiveness and brand loyalty. The experimental results show that this combination method shows significant advantages in multiple dimensions. By analyzing the user interaction data of different social platforms, by analyzing the user interaction data of different social platforms, it is found that the TikTok platform has the largest amount of user interaction (reaching 150,000 total interactions, including 100,000 likes), showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, especially in terms of the number of comments and shares. According to this data, fashion brands can pay more attention to interactivity with target consumers when choosing communication platforms. In the research of personalized communication strategy, through big data and artificial intelligence technology, fashion brands can accurately push content and make real-time adjustments according to consumers' interests and behaviors. Experiments show that personalized recommendation content can increase consumers' purchase intention to 30%, and improve consumers' satisfaction and repurchase intention. The quality analysis of narrative generation based on LLM shows that with the improvement of automated evaluation scores, the scores of multiple dimensions such as narrative, fluency, and coherence gradually increase, showing the great potential of large language models in fashion brand narrative generation. Combining the structured information of knowledge graphs with the text generation capabilities of LLM, brands can ensure the personalization and accuracy of communication content while maintaining creativity.</p>

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Research on fashion brand narrative generation and personalized communication strategy based on knowledge graph and LLM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Qiyuan Miao

摘要

With the continuous advancement of artificial intelligence technology, knowledge graphs and large language models have shown great potential in the communication strategies of fashion brands. This paper presents innovative approaches that combine the two, aiming to elevate fashion brands' narrative generation and personalized communication strategies. By building a model based on the combination of knowledge graph and LLM, fashion brands can better understand consumer needs and generate more accurate brand stories, thereby improving market responsiveness and brand loyalty. The experimental results show that this combination method shows significant advantages in multiple dimensions. By analyzing the user interaction data of different social platforms, by analyzing the user interaction data of different social platforms, it is found that the TikTok platform has the largest amount of user interaction (reaching 150,000 total interactions, including 100,000 likes), showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, showing a high degree of engagement of the platform. Other platforms like Instagram and Weibo have lower user interaction, especially in terms of the number of comments and shares. According to this data, fashion brands can pay more attention to interactivity with target consumers when choosing communication platforms. In the research of personalized communication strategy, through big data and artificial intelligence technology, fashion brands can accurately push content and make real-time adjustments according to consumers' interests and behaviors. Experiments show that personalized recommendation content can increase consumers' purchase intention to 30%, and improve consumers' satisfaction and repurchase intention. The quality analysis of narrative generation based on LLM shows that with the improvement of automated evaluation scores, the scores of multiple dimensions such as narrative, fluency, and coherence gradually increase, showing the great potential of large language models in fashion brand narrative generation. Combining the structured information of knowledge graphs with the text generation capabilities of LLM, brands can ensure the personalization and accuracy of communication content while maintaining creativity.