中文摘要：

为了开发更加准确高效的学科新兴趋势探测方法，必须加强科研主题演化规律的研究。本文提出了一种新的基于共词网络社区演化分析的研究框架。我们基于社区主题表示算法和社区相似度匹配算法，构建了一个科研主题演化分析模型，并开发了一款新颖的网络社区演化分析软件NEViewer。与已有的科学图谱分析软件相比，NEViewer的创新在于：（a）设计一套时序网络社区演化分析框架；（b）实现了多个网络社区演化分析算法；（c）以冲积图和赋色网络图两种创新性的方式揭示了网络社区演化的宏观过程和微观细节。利用NEViewer对中文计算机学科进行的实验结果表明NEViewer在复杂网络社区演化可视化分析上是可靠的和有效的，借助共词网络进行学科主题演化研究的思路也是可行的。

英文摘要：

The evolution rules of research topics in a discipline is the key to develop new emerging trend detection methods. This paper proposes a new research frame based on co-word network evolution analysis. The knowledge structure of a discipline can be expressed by a special co-word network communities in that network mean topics. A topic evolution analysis model is created based on community match mechanism. NEViewer presents three key features that are remarkable compared to other science mapping software tools： （a） a powerful analyzing module within a longitudinal framework; （b） the use of several network community evolutions analyzing algorithms; （c） revealing the macroscopic shifts and microcosmic details of evolution based on alluvial diagram and colored network. The result from an experiment within Chinese computer science field showed that NEViewer is effective and liable. The research process, using co-word network analysis research topics evolution in disciplines, is also feasible.