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Model-Driven Integration of Deep Learning for Artifact Classification in Museum Information Systems

Author

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  • Ke Xu

    (Hebei Minzu Normal University, China)

  • Qiong Wu

    (Chifeng University, China)

  • Yujiao Hou

    (Chifeng University, China)

Abstract

Museum Information Systems (MIS) often rely on manual classification and keyword search, limiting accuracy and scalability. Deep learning offers a solution, but effective integration requires alignment with curatorial workflows. This study proposes a model-driven framework for integrating Convolutional Neural Networks (CNNs) into MIS to enhance artifact classification and retrieval. A prototype was built using ReactJS, Django, and TensorFlow, and it was trained on a curated subset of The Met's Open Access Images. The system employs a Hybrid-E Loss for improved classification accuracy. The model achieved 94.3% classification accuracy and real-time retrieval latency below 100 ms, with throughput exceeding 14 queries per second. The framework successfully bridges AI performance with curatorial logic, demonstrating a scalable and interpretable solution for digital heritage systems.

Suggested Citation

  • Ke Xu & Qiong Wu & Yujiao Hou, 2025. "Model-Driven Integration of Deep Learning for Artifact Classification in Museum Information Systems," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global Scientific Publishing, vol. 20(1), pages 1-24, January.
    • Handle: RePEc:igg:jitwe0:v:20:y:2025:i:1:p:1-24
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