  Identifying and predicting the factors that contribute to the success of
interdisciplinary research is crucial for advancing scientific discovery.
However, there is a lack of methods to quantify the integration of new ideas
and technological advancements in astronomical research and how these new
technologies drive further scientific breakthroughs. Large language models,
with their ability to extract key concepts from vast literature beyond keyword
searches, provide a new tool to quantify such processes. In this study, we
extracted concepts in astronomical research from 297,807 publications between
1993 and 2024 using large language models, resulting in a set of 24,939
concepts. These concepts were then used to form a knowledge graph, where the
link strength between any two concepts was determined by their relevance
through the citation-reference relationships. By calculating this relevance
across different time periods, we quantified the impact of numerical
simulations and machine learning on astronomical research. The knowledge graph
demonstrates two phases of development: a phase where the technology was
integrated and another where the technology was explored in scientific
discovery. The knowledge graph reveals that despite machine learning has made
much inroad in astronomy, there is currently a lack of new concept development
at the intersection of AI and Astronomy, which may be the current bottleneck
preventing machine learning from further transforming the field of astronomy.
