Since the beginning of the 21st century, the fields of astronomy and astrophysics have experienced significant growth at observational and computational levels, leading to the acquisition of increasingly huge volumes of data. In order to process this vast quantity of information, artificial intelligence (AI) techniques are being combined with data mining to detect patterns with the aim of modeling, classifying or predicting the behavior of certain astronomical phenomena or objects. Parallel to the exponential development of the aforementioned techniques, the scientific output related to the application of AI and machine learning (ML) in astronomy and astrophysics has also experienced considerable growth in recent years. Therefore, the increasingly abundant articles make it difficult to monitor this field in terms of which research topics are the most prolific or novel, or which countries or authors are leading them. In this article, a text-mining-based scientometric analysis of scientific documents published over the last three decades on the application of AI and ML in the fields of astronomy and astrophysics is presented. The VOSviewer software and data from the Web of Science (WoS) are used to elucidate the evolution of publications in this research field, their distribution by country (including co-authorship), the most relevant topics addressed, and the most cited elements and most significant co-citations according to publication source and authorship. The obtained results demonstrate how application of AI/ML to the fields of astronomy/astrophysics represents an established and rapidly growing field of research that is crucial to obtaining scientific understanding of the universe.

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机器学习在天文学和天体物理学中的应用：基于文本挖掘的科学计量分析

自 21 世纪初以来，天文学和天体物理学领域在观测和计算水平上经历了显着增长，导致获取的数据量越来越大。为了处理大量信息，人工智能 (AI) 技术正在与数据挖掘相结合，以检测模式，目的是对某些天文现象或物体的行为进行建模、分类或预测。在上述技术呈指数级发展的同时，与人工智能和机器学习（ML）在天文学和天体物理学中的应用相关的科学产出近年来也经历了相当大的增长。所以，越来越丰富的文章使得很难监测该领域的哪些研究主题最多产或最新颖，或者哪些国家或作者处于领先地位。在本文中，介绍了对过去三年发表的有关 AI 和 ML 在天文学和天体物理学领域应用的科学文献的基于文本挖掘的科学计量分析。VOSviewer 软件和来自 Web of Science (WoS) 的数据用于阐明该研究领域出版物的演变、它们按国家/地区的分布（包括共同作者）、涉及的最相关主题以及被引用次数最多的元素和最多根据出版物来源和作者身份的显着共同被引。