[Purpose/Significance] As a major developmental direction of next-generation nucleic acid therapeutics, mRNA-based therapies are characterized by a high degree of technological convergence and rapid iterative advancement. A systematic analysis of the technological development trajectory in the field of mRNA therapeutics, including the identification of key technological themes and the elucidation of their dynamic evolutionary patterns, is of significant theoretical value and practical relevance for understanding the innovation frontier and overall development trends of this field. [Method/Process] Using patent data from the field of mRNA therapeutics as the object of study, this research constructed a technical text-mining framework that integrated deep learning algorithms with large language models to enable the automated extraction of key technological entities in the mRNA therapeutics domain. On this basis, the Latent Dirichlet Allocation (LDA) topic modeling method was employed to identify topics and analyze their intensities across patent data from different time periods, thereby revealing the stage-wise evolutionary characteristics and developmental trajectories of the major technological themes. [Result/Conclusion] Technological development in the field of mRNA therapeutics exhibits a clear stage-wise progression characterized by “basic research-application expansion-clinical translation.” From the perspective of technological themes, cancer immunotherapy, RNA modification, drug delivery systems, vaccine development, and gene editing successively emerge as focal research areas at different stages. Specifically, the early stage of research primarily focuses on enhancing mRNA stability and optimizing chemical modification strategies; the intermediate stage gradually expands toward the design of delivery carriers and improvements in delivery efficiency; in recent years, vaccine development and broad-spectrum applications targeting multiple indications have become the dominant directions.