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<p>Technological convergence refers to the phenomenon where boundaries between technological areas and disciplines are increasingly blurred. It enables the integration of previously distinct domains and has emerged as&#xa0;a mainstream trend in today’s innovation process. However, accurately measuring technological convergence remains a persistent challenge due to its inherently multidimensional and evolving nature. This study develops&#xa0;an AI-enhanced Technological Convergence Index (TCI) that captures convergence along two fundamental dimensions: depth and breadth. For the&#xa0;depth&#xa0;dimension, we use IPC textual descriptions as the analytical foundation and enhance this assessment by incorporating supplementary patent metadata into a heterogeneous graph structure. This graph is then modeled using Heterogeneous Graph Transformers (HGT) combined with Sentence-BERT (SBERT), enabling a precise representation of knowledge integration across technological boundaries. Complementing this, the breadth dimension captures the diversity of technological fields involved, quantified using the Shannon Diversity Index (SDI) to measure the variety of technological combinations within patents. The final TCI is constructed using the Entropy Weight Method (EWM), which objectively assigns weights to both dimensions based on their information entropy. To validate our approach, we compare the proposed TCI with established convergence measures, demonstrating its comparative advantages. We further establish empirical reliability through a novel robustness test that regresses TCI against indicators of patent quality. Applying this framework to Chinese patents related to the twin transition (2003–2024) reveals that technological convergence has a significant positive effect on patent quality, confirming that higher levels of technological convergence are associated with higher-quality innovations. These findings are further supported by comprehensive robustness checks. Our multidimensional approach provides valuable practical insights for innovation policy and industry strategies in managing emerging cross-domain technologies.</p>

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AI-enhanced multi-dimensional measurement of technological convergence through heterogeneous graph and semantic learning

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Siming Deng,
  • Runsong Jia,
  • Chunjuan Luan,
  • Mengjia Wu,
  • Yi Zhang

摘要

Technological convergence refers to the phenomenon where boundaries between technological areas and disciplines are increasingly blurred. It enables the integration of previously distinct domains and has emerged as a mainstream trend in today’s innovation process. However, accurately measuring technological convergence remains a persistent challenge due to its inherently multidimensional and evolving nature. This study develops an AI-enhanced Technological Convergence Index (TCI) that captures convergence along two fundamental dimensions: depth and breadth. For the depth dimension, we use IPC textual descriptions as the analytical foundation and enhance this assessment by incorporating supplementary patent metadata into a heterogeneous graph structure. This graph is then modeled using Heterogeneous Graph Transformers (HGT) combined with Sentence-BERT (SBERT), enabling a precise representation of knowledge integration across technological boundaries. Complementing this, the breadth dimension captures the diversity of technological fields involved, quantified using the Shannon Diversity Index (SDI) to measure the variety of technological combinations within patents. The final TCI is constructed using the Entropy Weight Method (EWM), which objectively assigns weights to both dimensions based on their information entropy. To validate our approach, we compare the proposed TCI with established convergence measures, demonstrating its comparative advantages. We further establish empirical reliability through a novel robustness test that regresses TCI against indicators of patent quality. Applying this framework to Chinese patents related to the twin transition (2003–2024) reveals that technological convergence has a significant positive effect on patent quality, confirming that higher levels of technological convergence are associated with higher-quality innovations. These findings are further supported by comprehensive robustness checks. Our multidimensional approach provides valuable practical insights for innovation policy and industry strategies in managing emerging cross-domain technologies.