This study proposes a three-tiered, multidimensional analytical framework for large-scale political speech corpora, integrating topic modeling, strategic narrative extraction, frame analysis, and temporal evolution modeling to systematically reveal the semantic structures and narrative dynamics of political discourse across different policy scenarios and major event cycles. Based on 426 public speeches delivered by a national leader between 2000 and 2022, this study constructs a 2.3-million-token corpus processed using dependency parsing, named entity recognition (NER), and event extraction. A dual-model approach combining LDA and BERTopic is used to identify core issues, while four strategic narrative elements—Self, Other, Goals, and Means—are extracted using a large language model (LLM). At the frame-analysis level, five frame types and their framing devices, including metaphor, analogy, causal chains, and emotional mobilization, are proposed, and the Frame Strength Index (FSI) is introduced to quantify frame salience. The experimental results show that economic frames dominate over the long term, while security and geopolitical frames increase significantly during crises; the LLM achieves an accuracy of 79–95% in rhetorical structure detection. At the temporal-evolution level, this study reveals the dynamic mechanisms of political discourse during the financial crisis, geopolitical conflicts, and the pandemic outbreak through topic drift, embedding shift, \(\Delta\)FSI, emotional polarity, and modal changes, revealing a process of “theme convergence–narrative reinforcement–command enhancement.”
