AI-POWERED CULTURAL HERITAGE REVITALIZATION
Dynamic Design of Zhuxian Town Woodblock New Year Paintings with AHP-KE and AIGC
This study pioneers a hybrid framework, fusing Analytic Hierarchy Process (AHP), Kansei Engineering (KE), and Artificial Intelligence Generated Content (AIGC) to breathe new life into intangible cultural heritage. Our method transforms traditional motifs into contemporary designs that resonate with younger audiences, showcasing AI's transformative power in cultural preservation.
Key Performance Indicators
Our innovative approach delivered significant improvements in both cultural relevance and market appeal for intangible cultural heritage.
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Higher Audience Appeal
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Cultural Fidelity Index
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Design Iteration Speed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction & Vision
Hybrid Methodology
AHP Demand Modeling
Kansei Affective Analysis
AIGC Design Practice
Challenges & Future
Introduction & Vision
Recent breakthroughs in AIGC, powered by deep-learning architectures, are transforming visual design. However, safeguarding intangible cultural heritage like Zhuxian Town's woodblock New Year prints faces challenges: intricate techniques, high costs, aging masters, and declining youth interest. This study proposes an AHP-KE framework enhanced by AIGC to overcome these barriers, offering a viable path for preservation and revival. We fuse qualitative insight with empirical rigor, deciphering the prints' "digital genetic code" for modern transformation.
Hybrid Methodology
Our research integrates three core components: Analytic Hierarchy Process (AHP) for systematic, multi-objective decision-making; Kansei Engineering (KE) to translate subjective emotional responses into objective product specifications; and Artificial Intelligence Generated Content (AIGC) for design generation. The workflow involves constructing a hierarchical model, Kansei sampling, AIGC image generation, and comparative validation, ensuring culturally faithful and market-relevant designs.
AHP Demand Modeling
Focused on consumers aged 18-30, our AHP model aligns heritage values with contemporary consumption patterns across three tiers: goal, criteria, and alternatives. Criteria include product attributes, cultural value, user experience, and brand equity. Expert deliberation determined indicator weights, identifying "Emotional Attachment" as top priority, followed by "Innovation" and "Balance of Tradition and Form." This provides a quantifiable roadmap for design intervention.
Kansei Affective Analysis
A sample library of five representative woodblock prints was curated. An exhaustive corpus search and expert review distilled 45 adjective pairs into 12 core Kansei adjective pairs, which were evaluated using a seven-point semantic-differential scale by 104 participants. Statistical analysis confirmed excellent internal consistency (Cronbach's α = 0.917) and suitability for factor analysis (KMO = 0.752), revealing "Form-Colour" and "Cultural-Semantic" as dominant factors.
AIGC Design Practice
AHP weights and Kansei insights guided the generation of prompt statements for diffusion-based AI models like Midjourney V6.1 and GPT-40. Key priorities included safeguarding symbolic motifs, injecting creative ideas, and harmonizing tradition with modern form. For instance, prompts like "Vector illustration of the Chinese God of Wealth in a bright, festive woodblock-print vibe... -p y5ytgl2 -raw" were used. Comparative validation showed AI-tuned designs achieved approximately 40% higher ratings than originals, proving their superior appeal.
Challenges & Future
While promising, AIGC for ICH faces limitations: scattered data, technical hurdles in decoding complex symbolism, copyright disputes over AI-generated works from collective heritage, and risks of cultural identity disruption. Future work should focus on developing rich cultural-context models, enhancing AI's storytelling accuracy, and establishing robust frameworks for copyright protection and ethical oversight to truly respect and renew traditional culture.
Enterprise Process Flow
Preliminary research
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Clearly define the design positioning
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Determine the overall design objective
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Target layer
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Criterion layer
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Solution layer
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Consistency check
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Ranking of indicator weights
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Building a sample library
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Collect sensory vocabulary
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Semantic Difference Scale
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Data statistical analysis
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Establish element correspondence
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Design practice
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Comparison verification
40%
Increase in Audience Preference for AI-Enhanced Designs
| Feature | Traditional Design (Avg. Score) | AI-Enhanced Design (Avg. Score) | Key Improvement (AI-Enhanced) |
|---|---|---|---|
| Modern Aesthetic | 3.2 | 5.6 | More in line with young Aesthetics |
| Emotional Expression | 4.2 | 5.8 | More vivid emotional expression |
| Cultural Narrative Clarity | 3.3 | 5.1 | Clearer cultural narrative |
| Color Matching | 3.5 | 4.6 | More harmonious color matching |
| Novel Style | 3.2 | 4.7 | More novel style |
Case Study: Revitalizing Zhuxian Town Woodblock New Year Prints
Our groundbreaking research transformed Zhuxian Town's traditional woodblock New Year prints by integrating AHP-KE methodology with cutting-edge AIGC tools. This approach successfully balanced cultural authenticity with contemporary aesthetic appeal, leading to designs that resonated significantly more with younger audiences. The validation process showed a remarkable 40% increase in preference for the AI-enhanced versions, demonstrating the practical efficacy of our dynamic design framework for intangible cultural heritage.
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Your AI Implementation Roadmap
A structured approach to integrating AI into your cultural design and preservation workflows.
Phase 1: Discovery & Strategy
Conduct comprehensive literature review, ethnographic studies, and AHP model construction to define strategic goals and capture expert panel insights for cultural preservation and innovation.
Phase 2: Affective Profiling
Compile Kansei vocabulary, administer semantic differential surveys to target audiences, and perform Principal Component Analysis to identify dominant affective factors driving design preferences.
Phase 3: AIGC Model Training & Prompt Engineering
Leverage AHP weights and Kansei insights to generate precise, targeted prompts for AI image models. Implement style transfer techniques to reimagine traditional motifs with contemporary aesthetics.
Phase 4: Design Validation & Refinement
Execute comparative testing of original versus AI-generated designs with user groups. Analyze preference scores and statistical tests to validate appeal and iteratively refine design outputs based on feedback.
Phase 5: Scaling & Dissemination
Develop strategies for wider adoption of AI-enhanced heritage designs. Address ethical considerations regarding copyright, cultural identity, and ensure sustainable, innovative dissemination practices.
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