## 1 Introduction
In dynamic topic modeling, this feature can help in tracking how topics change over time. Despite its ability to handle complex semantic representations, significant issues arise when applying BERTopic for topic modeling in patent-related technologies, especially within the rapidly changing and complex 6G landscape. Firstly, the presence of substantial noise in the patent data, compounded by challenges in accurately understanding domain-specific technological terms, poses a significant hurdle. These factors either lead to the model's failure to cluster key technological points effectively or result in overly general clustering outcomes. Consequently, the model struggles to precisely identify and represent the core technological features within the 6G domain, undermining the effectiveness of topic-based analysis in this highly specialized and dynamic field. Additionally, detecting early signs of emerging trends in the fast-evolving landscape is crucial for strategic planning. These early signs, which are referred to as weak signals [22], indicate potential but unconfirmed changes. They may later become significant indicators of critical forces, characterized by low terms and document frequencies but high growth rates, and can signal the emergence or disruption of a target technology in 6G, even with minimal current impact. The existing DTM-based applications, such as RollingLDA [23], also face challenges in identifying and tracking emerging signals at a given moment rather than in a long-term evolution. This challenge necessitates that the model adapts to the crucial temporal dynamics of emerging trends in a complex technological landscape and demonstrates interpretability regarding shifts in industry focus. Moreover, the topics generated by these DTM-based methods often fail to provide meaningful insight for interpreting emerging technologies to enhance strategic planning. To overcome these challenges, we propose a novel dynamic topic modeling framework in patent analysis, which employs Large Language Models (LLMs) to enhance the dynamic topic modeling framework to capture the semantics of evolving topics in a complex technological landscape and identify the emerging technology trend in a more interpretable manner. In this study, we collected 78,625 patents in the field of 6G. We performed a prompt-based technological summarization with LLMs to effectively filter out irrelevant information, thereby significantly enhancing the performance of subsequent text clustering models. Then, we conducted a comparative analysis of the performance of various embedding techniques, such as word2 vec, doc2 vec, BERT, and PatentsBERTa, against the baseline results and utilized contextual embedding techniques to capture semantics and extract topics in the text data. Furthermore, we adopted an enhanced BERTopic-based dynamic topic modeling framework for technological evolution analysis with random-search-based hyperparameter tuning, which can better capture the underlying temporal relationships between topics over time. Finally, to quantitatively identify the emerging trend and interpret the results to obtain technological insights in this field, we performed the weak sign analysis on the generated topics, enabling us to identify the development trends of technologies in Topic Emerging Maps (TEMs). The main contributions of this paper are as follows:  
* We proposed a novel dynamic topic modeling framework in patent analysis, which employs an enhanced BERTopic-based approach with hyperparameter optimization to capture the temporal relationship and semantics of evolving topics. Additionally, we utilized LLMs to pre-process the redundant patent data to significantly reduce the noise, thereby enabling automatic tracking of the evolving trends in a complex landscape;
* By conducting a comparative analysis of various embedding techniques, we selected the SBERT model specially pre-trained on patent data combined with temporal aligned clustering; we achieved better semantic capture and more accurate evolving topic extraction over time.