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

This research paper proposes an AI-based predictive model that uses deep learning and text mining techniques to forecast visitor numbers at art museums. The study employs eight deep learning algorithms—including RNN, LSTM, and Transformer—to analyze unstructured textual data from museum websites, visitor comments, and social media, integrating a Balanced Scorecard framework with four strategic perspectives: social value, visitor experience, exhibition management, and art education.