Application interface design of Chongqing intangible cultural heritage based on deep learning

Area of Science:

• Digital Humanities
• Computer Science
• Cultural Heritage Studies

Background:

• Integrating intangible cultural heritage (ICH) protection into smart city initiatives is crucial for urban cultural enrichment.
• Existing applications lack effective ICH recognition and user engagement features.

Purpose of the Study:

• To design an interactive smart city application interface for intangible cultural heritage (ICH) protection.
• To improve the cultural experience for urban residents and tourists through enhanced heritage engagement.

Main Methods:

• Classification and analysis of Chongqing's ICH and existing ICH-related mobile applications (APPs).
• Development of a deep learning (DL) based image recognition algorithm for ICH identification.
• Design of an APP interface incorporating user habits and visual perception principles.

Main Results:

• Convolutional Neural Networks (CNNs) significantly improved ICH image recognition accuracy, recall, and F1 scores.
• Transfer learning (TL) further enhanced the performance of the DL model.
• The developed Chongqing ICH APP demonstrated superior user experience and usability.

Conclusions:

• The DL-powered ICH application interface effectively integrates cultural heritage into smart cities.
• The study provides a model for enhancing user engagement and usability in cultural heritage applications.
• Future work should focus on optimizing recognition algorithms and integrating immersive technologies like VR.