[Objective] This paper examines the evolution of research topics, which helps researchers quickly identify the status quo and trends in their fields. [Methods] First, we merged multi-source datasets and divided the domain research topics by time period. Then, we calculated topic importance with their popularity, density, and closeness centrality. Third, we utilized topic semantic similarity to identify the related ones from adjacent time periods. Finally, we combined the topic importance fluctuation and the topic similarity to decide their evolution types and paths. [Results] We examined our model with papers on artificial intelligence and analyzed the changes of topics in the past 20 years. We identified the popular research topics and their evolution paths, which showed obvious thematic fusion and split development in four periods. [Limitations] The topic naming rules could be more effective and we could not show the whole life cycle of the booming artificial intelligence research. [Conclusions] The proposed model could effectively reveal the topic evolution of research.
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