【目的/意义】 基于传统主题模型与大语言模型结合以及基于RAG的大语言模型设计主题建模方法，分析新兴主题和非新兴主题的扩散情况，为后续新兴主题的识别和扩散提供参考。 【方法/过程】 首先，通过传统主题模型和大语言模型结合的方法对专利进行主题挖掘，根据新颖性、连续性、增长度和影响力识别新兴主题；其次，利用RAG微调的大语言模型评分，确定最佳主题建模方法；最后，构建引文网络计算扩散速度、广度、强度、深度和地域，对新兴主题和非新兴主题扩散指标的时序变化情况进行分析。 【结果/结论】 以核聚变为实证领域，研究发现基于BERTopic+Doubao的主题建模方法效果最佳；同时新兴主题扩散呈现波动上升的趋势，非新兴主题则呈现先稳步上升后小幅下降的趋势。本文提出的传统主题模型和大语言模型结合的方法能够有效识别新兴主题，RAG微调的大语言模型优化了结果的评估效果，且丰富了主题扩散测度视角。

[Purpose/Significance] Based on the combination of traditional topic models and large language models, as well as RAG-based large language model, this study designs the topic modeling method, analyzes the diffusion of emerging topics and non-emerging topics, so as to provide a reference for the subsequent identification and diffusion of emerging topics. [Methods/Process] First of all, the topic mining of patents was carried out through the combination of traditional topic models and large language models, and the emerging topics were identified based on novelty, continuity, growth and influence. Secondly, the best topic modeling method was determined by using the large language model score fine-tuned by RAG. Finally, a citation network was constructed to calculate the diffusion speed, breadth, intensity, depth, and region, and analyze the temporal changes of the diffusion indicators of emerging topics and non-emerging topics. [Result/Conclusion] Taking nuclear fusion as the empirical field, the research finds that the topic modeling method based on BERTopic+Doubao has the best effect. At the same time, the diffusion of emerging themes mostly shows a fluctuating upward trend, while non-emerging themes show a steady upward trend first and then a slight downward trend. The method of combining the traditional topic model and the large language model proposed in this paper can effectively identify emerging topics. The RAG fine-tuning large language model optimizes the evaluation effect of the results, and enriches the perspective of topic diffusion measurement.