Uncovering multi-technology convergence patterns with hypergraphs: Evolution and prediction using patent data

Technology convergence integrates distinct domains to create novel combinations, driving radical innovation that reshapes markets and industries. However, prevailing approaches rely on pairwise networks that cannot capture multi-technology interactions and suffer scale biases from heterogeneous patenting activity. To overcome these limitations, this study proposes a hypergraph-based framework that directly models multi-technology convergence and identifies statistically significant convergence via a probabilistic null model. Using four decades of USPTO patent data (1984–2023), we construct two comparable hypergraphs: a co-classification hypergraph representing explicit inventive convergence and a co-citation hypergraph capturing implicit knowledge-flow convergence. Evolution analysis on both hypergraph types reveals a sustained growth in multi-technology and cross-domain convergence, with a marked transition from chemistry-led to computing-led convergence patterns. Building on these insights, we formulate the forecasting of technology convergence as a hyperedge prediction task. We implement random forest classifiers trained on two complementary feature sets derived from both hypergraphs: similarity features capturing structural and semantic similarities among technologies, and intrinsic features representing inherent attributes of the constituent technologies. Predictive results demonstrate that features derived from the co-citation hypergraph exhibit stronger predictive power than those from the co-classification hypergraph. Their combination achieves optimal performance, with both AUC-ROC and AUPRC exceeding 0.90. Explainable AI analyses (Gini importance and SHAP) identify similarity features, including global knowledge-flow reachability and semantic similarity, as dominant drivers of convergence, while citation and economic values in intrinsic features exhibit contrasting associations with convergence probability. This framework bridges evolution analysis and predictive modeling of multi-technology convergence, providing actionable intelligence for anticipating technological opportunities and guiding innovation strategy.