In the evolving landscape of library services, propelled by advancements in Internet technology and service paradigms, this study utilizes cloud-based lending data from college libraries to improve user profiling and subject-specific lending. Integrating the K-means algorithm with a Boolean matrix-enhanced Apriori algorithm, we devise a data mining model that fine-tunes detecting patterns in user borrowing behaviors. This approach distinguishes five distinct subject areas: energy, computing, electronic communication, machinery, and environmental chemistry. The outcome reveals a bibliographic association rule mining confidence of up to 79.38%, a 30% increase over conventional methods. Moreover, it generates three notable 2-item sets. Our model introduces a groundbreaking way to offer personalized library services, significantly enriching the user experience with tailored subject information.