Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation. Unlike previous studies focusing on the binary relationship between a paper and the concept it attributes to technology, our approach utilizes attribution scores to enhance the relationships between research papers, combining keywords, citation rates, and collaboration status with specific technological concepts. The proposed method integrates text mining and bibliometric analyses to formulate and predict technological proximity indices for encryption technologies using the "OpenAlex" catalog. Our case study findings highlight a significant convergence between blockchain and public-key cryptography, evidenced by the increasing proximity indices. These results offer valuable strategic insights for those contemplating investments in these domains.

翻译：识别网络安全新兴技术之间的技术趋同性，对推动科学发展与促进创新至关重要。与以往聚焦论文与其所归属技术概念之间二元关系的研究不同，本文利用归因得分增强研究论文与特定技术概念之间的关联，综合关键词、引用率及合作状态等多维要素。所提出的方法融合文本挖掘与文献计量分析，基于OpenAlex数据集构建并预测加密技术的邻近指数。案例研究结果显示，区块链与公钥密码学之间存在显著趋同性，其邻近指数持续增长。这些发现为有意投资上述领域的主体提供了具有战略价值的洞察。