Visual analysis of user portraits in China digital library from 2013 to 2024

Purpose This study reviews user portrait research in Chinese digital libraries (2013–2024), exploring links between library services and multi-dimensional user needs to inform data-driven service optimization. Design/methodology/approach A topic concept lattice with fuzzy formal context was constructed using multilingual literature. By analyzing topic intensity (i.e. topic prevalence across documents) and topic depth (i.e. hierarchical position in the concept lattice) by inducing fuzzy association rules, we addressed the limitations of single-method approaches in previous research. Findings Topics like user request, behavior and experience dominate, reflecting personalization trends. Chinese literature emphasizes user request, while international work focuses on information retrieval and data mining. Originality/value It effectively makes up for the deficiencies of previous studies, which either relied on single data sources LDA modeling without considering topic fuzziness or used FCA alone without probabilistic analysis. The study introduces a novel integration of FFCA and LDA, featuring adaptive parameter tuning and domain-specific validation in digital libraries that integrates probabilistic topic modeling with fuzzy concept lattice analysis.