This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies through the use of centrality measures, clustering coefficients, and degree of fusion measures. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape and the maturity level of the domain. The results of this study have significant implications for future development and advancements in artificial intelligence and provide a clear understanding of key technology areas of fusion. Furthermore, this paper contributes to AI public policy research by offering a data-driven perspective on the current state and future direction of the field. However, it is important to acknowledge the limitations of this research and call for further studies to build on these results. With these findings, we hope to inform and guide future research in the field of AI, contributing to its continued growth and success.

翻译：本文利用中心性度量、聚类系数和融合度度量，研究了多种人工智能技术之间的互联性与相互依赖性。通过在不同时间窗口内分析这些技术并量化其重要性，我们揭示了塑造人工智能格局的关键组成部分及该领域成熟度的重要洞见。本研究的结果对人工智能未来发展与进步具有重大意义，并为关键融合技术领域提供了清晰认识。此外，本文通过提供关于该领域现状及未来方向的数据驱动视角，为人工智能公共政策研究做出了贡献。但需承认本研究的局限性，并呼吁在现有成果基础上开展进一步研究。基于这些发现，我们旨在为人工智能领域的未来研究提供参考与指导，助力其持续发展与成功。