【摘要】 识别不同学科间共有的研究内容是学科交叉知识发现的一种研究思路。学科间具有相似语义的研究内容，能够更好地体现学科之间知识的融合、交流现象。针对从科技文献数据中获取语义相似学科交叉研究主题的问题，本文提出了一种基于无监督对比学习的科技文献及关键词语义相似关系表示学习方法，构建了一种语义相似学科交叉主题识别模型。该模型将Spearman相关系数作为评价学科交叉主题的指标，解决了现有研究缺少学科交叉研究数据集的问题。研究结果表明，本文模型较好地获取了科技文献及其关键词之间的语义相似关系，能够较好地反映两个学科之间的交叉态势。更多还原

【Abstract】 Identifying the research content shared among different disciplines is the research idea of interdisciplinary knowledge discovery. Research content with similar semantics better reflects the integration and exchange of knowledge between disciplines. To address the problem of obtaining semantically similar interdisciplinary research topics from scientific and technical literature data, this study proposes an unsupervised contrastive learning method for semantic similarity relationship representation learning of scientific and technical literature and keywords, and then constructs a semantically similar interdisciplinary topic identification model. The model uses the Spearman correlation coefficient as an index for evaluating interdisciplinary topics, thus addressing the lack of interdisciplinary research datasets in current research. Experiments reveal that the model correctly captures the semantic similarity relationship between scientific and technical literature and their keywords, and that the experimental results properly represent the intersection tendency between the two disciplines.更多还原