This paper proposes a modeling framework for dynamic topic evolution based on temporal large language models. The method first uses a large language model to obtain contextual embeddings of text and then introduces a temporal decay function and an attention mechanism. These components allow the model to adjust the importance of semantic units according to time intervals and capture topic variations across different periods. The temporal representations are then mapped into a latent topic space, where a state transition matrix is applied to describe the dynamic evolution of topics. A joint optimization objective constrains both semantic modeling and temporal consistency, ensuring diversity and smoothness in topic generation. The design emphasizes the unified modeling of semantic representation and temporal evolution, which improves topic coherence and diversity while enhancing stability and interpretability over time. Experiments on real-world corpora show that the framework effectively captures the generation, expansion, and decline of topics and outperforms existing models across multiple metrics. Overall, the proposed method provides a systematic solution for understanding dynamic semantic patterns in large-scale text, enriches the research paradigm of topic modeling, and supports complex text analysis tasks in multiple domains.

翻译：本文提出了一种基于时态大语言模型的动态主题演化建模框架。该方法首先利用大语言模型获取文本的上下文嵌入表示，随后引入时态衰减函数与注意力机制。这些组件使模型能够根据时间间隔调整语义单元的重要性，并捕捉不同时期内的主题变化。时态表征随后被映射至潜在主题空间，其中应用状态转移矩阵来描述主题的动态演化过程。通过联合优化目标同时约束语义建模与时态一致性，确保主题生成的多样性与平滑性。该设计强调语义表征与时态演化的统一建模，在提升主题连贯性与多样性的同时，增强了随时间变化的稳定性与可解释性。在真实语料上的实验表明，该框架能有效捕捉主题的生成、扩展与衰退过程，并在多项指标上优于现有模型。总体而言，所提方法为理解大规模文本中的动态语义模式提供了系统性解决方案，丰富了主题建模的研究范式，并支持多领域复杂文本分析任务。