Research on the Deep Feature Representation Method for Digital Management of Museums
Zheng Li · IEEE Access · 2026
This study introduces a symbolic, multi-layered approach that bridges semantic reasoning with dynamic digital environments. Existing solutions for managing structured cultural data often rely on rigid database schemas or brittle machine learning pipelines that struggle with semantic drift, context adaptation, and domain-specific fidelity. These traditional models lack mechanisms to maintain consistent, explainable knowledge evolution, and often fail to integrate heterogeneous inputs such as ontologies, visitor interactions, and metadata constraints. In response, we propose an integrated system architecture that comprises a symbolic repository layer, a formal reasoning engine, a temporal interaction graph, and a consistency controller—jointly orchestrated through a semantic framework named CuratoSynth. This orchestration enables adaptive knowledge alignment by encoding interpretive intentions and synthesizing them with evolving user behavior patterns. Our model ensures contextual integrity through logic-based rule enforcement and supports intelligent curatorial decision-making via automated inference and constraint propagation. Experimental simulations confirm the method’s robustness in sustaining ontological compliance and semantic continuity over time, significantly outperforming baseline approaches in both interpretive accuracy and system transparency. This work offers a scalable, explainable, and domain-grounded framework for the intelligent digital orchestration of cultural knowledge systems.