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Authors:
Xueli Yu
Haiyun Xu
Zhengyin Hu
Robin Haunschild
Zenghui Yue
Chunjiang Liu
Keywords: Emerging technologies; Multi-source data; BERTopic; Smart grid.
Abstract:
Emerging technologies (ETs) play a crucial role in scientific revolutions and industrial transformation. Accurate identification of ETs contributes to effective national policymaking and the rapid advancement of science and technology. However, existing studies primarily rely on single data sources, leading to limitations in timeliness and comprehensiveness of identification results. To address these shortcomings, this study proposes a novel approach for emerging technology identification that integrates multi-source data fusion with coordinated multi-dimensional indicators. First, heterogeneous multi-source data are collected, and candidate technology topics are extracted using BERTopic-based topic modeling. Second, semantic similarity analysis is employed to fuse technology topics across different data sources, generating a comprehensive set of candidate ETs and constructing an indicator system for their identification. Finally, ET topics are screened and identified. The results are subsequently validated. An empirical analysis in the smart grid domain identifies nine ETs. The findings provide important references for technology forecasting, policy formulation, and industrial strategic planning.
Pages: 16 to 23
Copyright: Copyright (c) IARIA, 2026
Publication date: May 24, 2026
Published in: conference
ISSN: 2519-8386
ISBN: 978-1-68558-397-2
Location: Venice, Italy
Dates: from May 24, 2026 to May 28, 2026