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.

该研究通过使用中心性度量，聚类系数和融合度量，研究了各种人工智能（AI）技术之间的相互联通性和相互依赖性。通过对不同时间窗口的技术进行分析，并量化其重要性，我们揭示了塑造 AI 领域和成熟度水平的关键组件，对未来的人工智能发展和进步具有重要的影响和启示作用。