Research on Academic Information Retrieval in the Age of Big Data and AI

Abstract

In the era of big data and artificial intelligence, academic information retrieval has undergone significant transformations. The data sources, retrieval methods and tools, as well as the presentation of search results, now differ substantially from traditional retrieval models. This paper analyzes how big data and AI technologies optimize academic information retrieval by expanding data sources and search scopes, enabling personalized search, semantic search, and associative search. It then delves into optimization strategies for traditional retrieval platforms to adapt to new technologies, including the integration of AI technologies into search engines and literature databases to achieve one-stop retrieval, enhancing network infrastructure improvements, and the need for users to develop big data literacy and AI literacy in this new technological landscape.