HU Zewen, HAN Yarong, ZHANG Xinyu, LIU Chang
LIBRARY TRIBUNE. 2026, 46(4): 74-91.
From a multi-source data perspective,this paper introduces a frontier topic mining/identification indicator system as well as a BERT-LDA model,based on academic papers,research grants,and patents. The study suggests that the system effectively identifies frontier topics within a research domain,and provides guidance for the data-driven quantitative identification of cutting-edge themes. Frontier topics in artificial intelligence span seven categories:machine learning and deep learning,natural language processing,computer vision,robotics,constraint and satisfaction problems,hardware and software,and applications. Frontier topics in various types of scientific and technological literature exhibit continuity and dynamic change during different time frames,with different types of literature emphasizing different frontier topics within the same period. In the field of artificial intelligence,frontier topics derived from different data sources exhibit stark evolutionary differences;and paper-based frontier topics vary considerably in evolutionary capacity across stages,with increasingly diverse developmental trajectories. Meanwhile,frontier topics reflected in research grants demonstrate pronounced evolutionary characteristics and diversification of development directions. Frontier topics reflected in patents show relatively minor differences during the evolutionary process,and their development directions are relatively stable. Significant differences exist between hotspots and emerging frontier topics emphasized at different stages throughout the evolutionary process.