The ability of Bayesian networks (BNs) to model complex systems and uncertainties makes it a perfect tool for the research on subfields related to climate change. In fact, in the past 30 years, BNs have been widely used in this field, with 1502 articles in total. Quantitatively understanding influential researchers, institutions, mainstream topics and research trends will help us quickly go deeper into this field. Thus, a scientometric method was conducted. In this paper, we identified the influential authors, journals, countries, institutions, topics and disciplines, key articles and research trends by collaboration network analysis, keyword co-occurrence network and document co-citation network analysis. As a result, we found that environmental sciences technology and water resources were the most popular research subfields, followed by energy fuels and meteorological atmospheric sciences. While as time goes on, research focuses have gradually shifted. Public environmental occupational health will become one of the most popular research subfields in the future.

贝叶斯网络（BN）对复杂系统和不确定性进行建模的能力使其成为研究气候变化相关子领域的完美工具。事实上，在过去的30年里，BN在该领域得到了广泛的应用，共有1502篇文章。定量了解有影响力的研究人员、机构、主流话题和研究趋势，将有助于我们快速深入该领域。因此，进行了科学计量方法。本文通过协作网络分析、关键词共现网络和文献共被引网络分析，识别了有影响力的作者、期刊、国家、机构、主题和学科、关键文章和研究趋势。结果，我们发现环境科学技术和水资源是最受欢迎的研究子领域，其次是能源燃料和气象大气科学。但随着时间的推移，研究重点逐渐发生转移。公共环境职业健康将成为未来最热门的研究子领域之一。