As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challenges such as ubiquitous connectivity, ultra-high data rates, and intelligent applications. Given the significance of 6G in shaping the future of communication and its potential to revolutionize various industries, understanding the technological evolution within this domain is crucial. Traditional topic modeling approaches fall short in adapting to the rapidly changing and highly complex nature of patent-based topic analysis in this field, thereby impeding a comprehensive understanding of the advanced technological evolution in terms of capturing temporal changes and uncovering semantic relationships. This study delves into the exploration of the evolving technologies of 6G in patent data through a novel dynamic topic modeling framework. Specifically, this work harnesses the power of large language models to effectively reduce the noise in patent data pre-processing using a prompt-based summarization technique. Then, we propose an enhanced dynamic topic modeling framework based on BERTopic to capture the time-aware features of evolving topics across periods. Additionally, we conduct comparative analysis in contextual embedding techniques and leverage SBERT pre-trained on patent data to extract the content semantics in domain-specific patent data within this framework. Finally, we apply the weak signal analysis method to identify the emerging topics in 6G technology over the periods, which makes the topic evolution analysis more interpretable than traditional topic modeling methods. The empirical results, which were validated by human experts, show that the proposed method can effectively uncover patterns of technological evolution, thus enabling its potential application to enhance strategic decision-making and stay ahead in the highly competitive and rapidly evolving technological sector.