Article: Deep learning driven recreation of traditional ethnic elements in animation works from the perspective of prototype theory Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.16 pp.38 - 52 Abstract: This article explores the innovative application of deep learning technology in re-imagining ethnic elements in animation, based on Jungian archetype theory. Addressing the homogenisation of traditional cultural symbols in animation amid globalisation, a three-dimensional creation model of 'archetype decoding-intelligent generation-cultural verification' is proposed. By building a deep neural network database of traditional patterns, mythological themes, and opera elements, and utilising generative adversarial networks (GANs) and variational autoencoders (VAEs), cultural archetypes are deconstructed and reassembled. Case studies demonstrate that this approach effectively extracts collective unconscious features from ethnic elements while preserving the spiritual core of cultural archetypes, generating innovative visual expressions with modern aesthetics. The research offers interdisciplinary insights for the innovative inheritance of cultural heritage from a digital humanities perspective and opens new technological pathways for animation creation in the AI era. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning driven recreation of traditional ethnic elements in animation works from the perspective of prototype theory

Authors: Bo Xing

Addresses: School of Fine Arts, Anyang University, Anyang 455000, China

Abstract: This article explores the innovative application of deep learning technology in re-imagining ethnic elements in animation, based on Jungian archetype theory. Addressing the homogenisation of traditional cultural symbols in animation amid globalisation, a three-dimensional creation model of 'archetype decoding-intelligent generation-cultural verification' is proposed. By building a deep neural network database of traditional patterns, mythological themes, and opera elements, and utilising generative adversarial networks (GANs) and variational autoencoders (VAEs), cultural archetypes are deconstructed and reassembled. Case studies demonstrate that this approach effectively extracts collective unconscious features from ethnic elements while preserving the spiritual core of cultural archetypes, generating innovative visual expressions with modern aesthetics. The research offers interdisciplinary insights for the innovative inheritance of cultural heritage from a digital humanities perspective and opens new technological pathways for animation creation in the AI era.

Keywords: deep learning; generate adversarial networks; variational autoencoder; VAE; animation creation.

DOI: 10.1504/IJICT.2025.146376

International Journal of Information and Communication Technology, 2025 Vol.26 No.16, pp.38 - 52

Received: 27 Mar 2025
Accepted: 11 Apr 2025
Published online: 27 May 2025 *

Title: Deep learning driven recreation of traditional ethnic elements in animation works from the perspective of prototype theory

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