Deep Learning models for cultural pattern recognition: preserving intangible heritage of Li ethnic subgroups through intelligent documentation systems

This study develops an advanced intelligent documentation system using deep learning models to preserve intangible cultural heritage for the Li ethnic minorities. Traditional heritage documentation models face significant obstacles in systematically capturing oral traditions and inter-group cultural differences. The proposed comprehensive multimodal fusion framework integrates visual pattern analysis through convolutional neural networks, temporal cultural depiction via bidirectional LSTM networks, and semantic comprehension using transformer-based models. Collaborative fieldwork across five Li subgroups (Ha, Qi, Run, Sai, and Meifu) in Hainan Province documented 4,450 cultural samples, including traditional textiles, music, oral traditions, artifacts, and architectural heritage. The five-layer distributed system architecture employs pattern recognition, semantic indexing, and recommendation algorithms for scalable cultural preservation. Experimental results demonstrate remarkable 94.8\% accuracy across Li subgroups, significantly outperforming traditional single-modality systems (CNN: 85.3\%, RNN: 87.6\%, Transformer: 89.4\%). System implementation yielded unprecedented improvements in cultural transmission effectiveness: 73\% increase in knowledge retention, 121\% in skill transfer, and 280\% in digital archiving abilities. Community participation increased exponentially, with 340\% growth in active users and a 665\% increase in monthly contributions. The system achieves robust operational performance with sub-200ms response times and 99.7\% stability. User satisfaction and expert evaluation scores of 4.4 and 4.6, respectively, confirm reliable cultural preservation functionality. This framework establishes advanced benchmarks for computational heritage preservation methods, demonstrating the effective integration of technological innovation with ethnographic sensitivity for the sustainable documentation and transmission of minority cultures.