Emotion-Driven Generative AI for Rural Cultural Memory Reconstruction: Mechanism and Design Framework
Year: 2026
Type: conference-paper
Abstract: In the context of digital cultural transformation and rural revitalization, rural cultural memory faces challenges of emotional disconnection and homogenized representation. While generative artificial intelligence (GAI) enables efficient content production, it often lacks the ability to capture culturally embedded emotional semantics. This study proposes an emotion-driven generative mechanism for rural cultural memory reconstruction from an emotional design perspective. A multi-source dataset integrating intangible cultural heritage, vernacular architecture, and folk customs is constructed, and an affective semantic modeling approach based on the Semantic Differential method and Kansei Engineering is developed. An emotion-parameter mapping mechanism is further established to embed emotional factors into the generative process. A human-AI collaborative framework is designed to support iterative optimization. Experimental results show that the proposed method significantly improves cultural consistency, emotional resonance, and design creativity$(\mathbf{p}<\mathbf{0. 0 5})$. The findings provide a computational and empirically validated approach for culturally grounded AI-driven design.
Authors: ZhiXian Xie, Chengxiang Yue
Institution: Guangdong Mechanical & Electrical Polytechnic
Language: English
Topic: Digital Media and Visual Art
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203870210