Purpose:This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level,covering a research topic’s evolution trend,evolution path and its content changes over time.Design/methodology/approach:After the topics were recovered by the author-topic model,we first built the keyword-topic co-occurrence network to track the dynamics of topic trends.Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics.It was used to illustrate the evolution path and content changes of research topics.A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.Findings:The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’evolution paths and content changes with the passage of time.Research limitations:Using the analysis framework will produce limited results when examining unstructured data such as social media data.In addition,the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.Practical implications:This analysis framework can help scholars and researchers map research topics’evolution process and gain insights into how a field’s topics have evolved over time.Originality/value:Tbe analysis framework used in this study can help reveal more micro evolution details.The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics,which belps better understand research topics’evolution paths and content changes. Purpose: This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level, covering a research topic’s evolution trend, evolution path and its content changes over time. Design / methodology / appach: After the topics were recovered by the author-topic model, we first built the keyword-topic co-occurrence network to track the dynamics of topic trends.. a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics. It was used to illustrate the evolution path and content changes of research topics. A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework. Findings: The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’ evolution paths and content changes with the passage of time. Research limitations: Using the analysis framework will produce limited results when examining unstructured data such as social media data. addition, the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in scientific fields. Practical implications: This analysis framework can help scholars and researchers map research topics’ evolution process and gain insights into how a field’s topics have evolved over time. Originality / value: Tbe analysis framework used in this study can help reveal more micro evolution details.The index to measure topic association strength defined in this paper includes both similarity and dissimilarity between topics, which belps better understand research topics’ evolution paths and content changes.