An Intelligent Machine Learning–Driven Smart Library Framework Using Self-Supervised Knowledge-Aware Transformers and Graph Neural Networks for Knowledge Management, Digital Humanities, and Lifelong Learning

For libraries to serve as intelligent digital ecosystems, they need an AI approach like advanced artificial intelligence can provide one with adaptive learning. It could turn libraries into intellectual digital ecosystems. The reliance of traditional library informatization systems on manual indexing and supervised models directly limits the scalability, transdisciplinary knowledge search platform mode. By taking an intelligent machine learning driven smart library framework as an example, this paper explores how to apply AI technologies (such as Self-Supervised Knowledge-Aware Transformer and Graph Neural Network-Based Knowledge Reasoning) intheir fields: administration of knowledge, digital humanities research and lifelong learning services. The SKAT approach combines field-specific ontologies and classification algorithms to achieve intelligent contextual understanding, while self-supervised learning extracts rich semantic representations of general meaning from large volumes of unlabeled texts, metadata, and user interaction records. The graph neural network is used to model and reason over library knowledge graphs. As a result, a complex web of relations linking information writers, categories, and readers together emerge. This comprehensive system supports personalized learning paths, automatic thematic classification, cultural content analytics and intelligent retrieval of resources. The suggested SKAT–GNN architecture outperforms only utilizing transformers (87.2%) as well as conventional models (83.4%) with a retrieval accuracy of 91.8%. It achieves improved conceptual comprehension and personalized learning performance with an F1-score of 91.5%, subject coherence of 0.67, and recommendation relevance (nDCG@10) of 0.81. On benchmark digital library datasets, experimental evaluation showed that its retrieval accuracy is higher than the traditional machine learning and transformer-only method. Moreover, it improves topic consistency in some measure. Lastly, recommendation relevance is enhanced as well. Smart libraries of the suggested framework make collaborative academic support and sustainable knowledge eco-systems feasible

Artificial neural network · Digital library · Intelligent agent · Knowledge base · Knowledge representation and reasoning · Knowledge-based systems · Lifelong learning · Search engine indexing · Semantic Web · Advanced Technologies in Various Fields · Big Data and Digital Economy · Library Science and Information