[目的/意义]近年来科研合作成为学术成果产出的重要途径之一，科研合作网络的链路预测成为提升科 研效率、促进学科资源融合的重要方式之一。但是现有研究鲜有将科研合作网络看作动态时变演化网络进行建 模，并考虑网络的文本语义属性和动态网络结构。[方法/过程]文章提出了融合文本语义信息和动态网络结构信 息的科研合作网络动态链路预测模型。首先，文章以统计学者为例，收集了国际统计四大期刊在2011—2020年 间发表的所有文章，基于论文合著关系构建了科研合作网络。其次，构建了科研合作网络的文本特征和动态结构 特征并对其进行了分析。最后，结合节点的语义特征和动态拓扑结构特征，本文使用动态逻辑回归对学者合作关 系进行链路预测。[结果/结论]结果表明，科研合作关系的动态演化受到多方面因素的共同影响，例如上一年度是 否合作、学者间的研究方向相似度、学者已发表论文的引用情况等。文章的研究结论对增强学者间的联系和提升 科研合作效率具有重要借鉴意义。

 [Purpose/significance] In recent years, the cooperation among scholars has become one of the important ways to output academic achievements.Co-authorship link prediction is one of the important topics to improve scientif? ic research efficiency and promote the integration of discipline resources. However, little studies have treated the coauthorship network as a dynamic time-varying network and integrated textual semantics and dynamic network informa? tion. [Method/process] Therefore, we propose a co-authorship link prediction model which incorporatenodes’unstruc? tured semantic attributes and dynamic network features.First, we take statisticians as an example and collect all papers in four top statistical journals from 2011 to 2020, which helps us construct a statisticians’co-authorship network. Sec? ond, we extract unstructured semantic attributes and dynamic network feature to enrich nodes representation. Finally, based on these features, we make a link prediction based on a dynamic logical regression model. [Result/conclusion] The results have shown that several factors can significantly influence co-authorship, such as whether two researchers cooperated last year, the research similarity between two researches, citation-related factors, etc. The result of this paper is of great significance to enhance the relationship between scholars and the efficiency of scientific research cooperation.