Trajectory-Aware Topic Mining: A Domain-Adaptive and Geometry-Preserving Framework for Identifying Promising Technologies

This study presents a trajectory-aware topic mining framework that addresses key limitations of existing trend analysis methods, including semantic information loss and parameter sensitivity. Leveraging domain-adaptive embeddings and geometry-preserving clustering, the framework preserves high-dimensional semantic structures while automatically determining optimal topic clusters. Self-attention mechanisms and cosine similarity enable accurate tracking of topic evolution over time. Additionally, dynamic metrics, velocity and directional consistency, quantify topic momentum and stability, allowing early identification of emerging or declining research themes. Applied to 206,536 computer vision publications (2012–2024), the framework effectively reveals rapidly evolving and stable subfields. The proposed approach offers actionable insights for researchers, industry practitioners, and policymakers to inform strategic R\&D investment and innovation planning.