Treffer: Optimization of University Library Services through Big Data and Multi-source Data Fusion

The advent of the big data era has not only advanced the informatization of libraries but also opened unprecedented opportunities for their sustainable development. Libraries are no longer limited to traditional resource management; instead, they have embraced emerging technologies such as Web 2.0, mobile solutions, cloud computing, resource discovery systems, and big data platforms. While these developments provide a solid technological foundation, libraries must further enhance their ability to conduct data analysis, semantic processing, decision-making, and visualization in order to respond effectively to evolving user demands and complex information environments. This study contributes to that goal by discussing the application of multi-source data fusion in science and technology decision-making. It presents a comprehensive decision support framework that integrates semantic preprocessing techniques—including data cleaning, partition segmentation, and synonym merging—supported by Python’s Pandas library and Jieba’s text-cutting functions. Through this approach, the research successfully identified six science and technology text clusters and three mass technology-related clusters, thereby providing a refined view of user information needs and thematic structures within large-scale datasets. The findings demonstrate that a decision support framework based on multi-source data fusion can proactively detect and respond to user needs, moving libraries from passive service providers to active, intelligent participants in knowledge dissemination. This proactive transformation enriches the quality of information services, enables accurate and personalized decision support, and aligns with the demands of the new era defined by innovation-driven and intelligence-first strategies. Ultimately, this work highlights the value of integrating big data technologies into library management and decision-making systems. By bridging semantic analysis with multi-source data fusion, libraries can evolve into dynamic hubs of ...