[Objective] This paper summarizes the research development trends of information retrieval, aiming to promote interdisciplinary studies and application of related technologies. [Methods] First, we used LDA model to identify topics of papers accepted by the SIGIR Annual Conference from 2008 to 2019. Second, we removed irrelevant papers based on the similarity between documents and topics, and grouped papers into multiple categories by calculating topic discrimination. Third, we constructed the evolution path of domain topics in time series which showed the increasing, decreasing and stable patterns. Finally, we created the fine-grained evolution path of a single topic through the modular community, which demonstrated the dynamic evolution process of knowledge units within the topics. [Results] The proposed method avoids the interference of irrelevant documents on identifying topics and evolution paths. The multi-topic classification of documents helps reveal the cross-fusion among topics. The current information retrieval research trends include user-centric, continuously optimized models, filtering and recommending, semantic web technology, deep learning methods, as well as medical and health information retrieval. [Limitations] It might be subjective to remove irrelevant documents and categorize documents with multi-topics. [Conclusions] Intelligent information services is becoming a new norm, and users’ needs for information retrieval becomes more prominent.