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Utilizing unstructured data to predict the art museum visitor numbers using deep learning approaches.

Ziyi Tian1, Xiao Wang2, Yuwei Tan3

  • 1Department of Art and Design, Daegu University, Gyeongsan, 38453, Republic of Korea.

Scientific Reports
|November 25, 2025
PubMed
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This summary is machine-generated.

This study introduces an adaptive Balanced Scorecard (BSC) for art museums, using AI to predict visitor numbers. Visitor Experience and Tourism Development (VED) strategies significantly boost attendance, with the Transformer model showing the best predictive accuracy.

Area of Science:

  • Museum Studies
  • Artificial Intelligence
  • Strategic Management

Background:

  • Art museums face challenges in quantifying strategic priorities and predicting visitor numbers.
  • Traditional strategic frameworks may not fully capture the nuances of museum operations and visitor engagement.
  • Integrating qualitative data from online sources is crucial for comprehensive strategic analysis.

Purpose of the Study:

  • To develop a modified Balanced Scorecard (BSC) framework specifically for art museums.
  • To utilize natural language processing (NLP) and text mining to extract strategic dimensions from unstructured data.
  • To build and evaluate deep learning models for predicting museum visitor numbers using both structured and unstructured data.

Main Methods:

  • An adaptive Balanced Scorecard framework was created with four dimensions: Social Values and Cultural Impact (SVCI), Visitor Experience and Tourism Development (VED), Exhibition and Operational Efficiency (EOE), and Art/Cultural Education and Sustainable Growth (ACES).
Keywords:
Art museumBalanced scorecardDeep learningText miningVisitor number prediction

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  • NLP and text mining techniques were employed to analyze museum websites and visitor-generated content.
  • Eight deep learning architectures (Transformer, LSTM, CNN, GAN) were implemented and compared for visitor number prediction, integrating numerical and text-derived variables.

    • Main Results:

    • Visitor Experience and Tourism Development (VED) was identified as the most influential strategic dimension for visitor attendance.
    • Interactive exhibitions, personalized experiences, and tourism collaborations within VED showed a strong positive impact.
    • The Transformer deep learning model demonstrated superior predictive performance in estimating visitor numbers.

    Conclusions:

    • The adapted BSC framework provides a systematic method for categorizing and quantifying art museum strategies.
    • Deep learning models, particularly the Transformer, can effectively integrate diverse data types for accurate visitor prediction.
    • The findings offer practical insights for art museum decision-making in visitor engagement, exhibition planning, and resource allocation.