THE CULTURAL RESILIENCE OF MACAO'S INTANGIBLE CULTURAL HERITAGE: THE MECHANISM OF IDENTITY RECONSTRUCTION IN THE INTEGRATION OF CHINESE AND WESTERN CULTURES

Abstract

Macau’s intangible cultural heritage (ICH) exemplifies a unique Sino-Western cultural fusion, wherein the interplay of Eastern and Western traditions complicates conventional analysis of heritage complexity and resilience. To address this challenge, we introduce FusionNet, a multimodal AI framework integrating image-based classification, an attention mechanism, identity embedding, and knowledge graph modeling for context-aware analysis of ICH. FusionNet combines image-based deep learning with an attention mechanism to focus on salient visual features in heritage imagery. This integrated architecture enables a holistic understanding of heritage elements and their adaptability to changing cultural contexts. Applied to Macau’s ICH, FusionNet reveals patterns of cultural resilience, illustrating how traditional practices persist and evolve amid centuries of East-West influences. Our findings demonstrate the efficacy of fusing visual and knowledge-based modalities for heritage analysis, offering a robust approach for studying and preserving intangible cultural heritage in complex cultural environments. To elucidate how Macau’s intangible cultural heritage (ICH) exhibits “cultural resilience” and the mechanisms of identity (re)construction amid Sino-Portuguese cultural interweaving; and to propose a computable multimodal framework (FusionNet + cultural-identity embeddings + knowledge graph) that quantifies and validates these mechanisms. Materials include digital archives and historical texts (e.g., Macau Memory), social-media text (Weibo plus ~1,000 English TripAdvisor/blog reviews), open heritage images, and structured knowledge bases (China ICH database). Methods comprise an attention-based image classifier (FusionNet), LDA topic modeling (5-fold cross-validation selecting k = 3; mean coherence ≈ 0.59, compared with BERTopic), bilingual sentiment analysis, knowledge-graph embedding and link prediction (evaluated with MRR, Hits@10), and t-SNE visualization with clustering (three clusters; average silhouette ≈ 0.47). All implementations are in Python. LDA reveals three stable themes: (A) Chinese traditions (~45%), (B) Lusophone heritage (~30%), and (C) hybrid/local identity (~25%; e.g., Patuá and Macanese cuisine). Sentiment analysis indicates >70% positive evaluations, with ~12–15% negative. On the image side, most categories achieve diagonal accuracy >0.80, with some true-positive rates reaching 0.95–1.00; Sino-Portuguese architecture shows interpretable confusion. Knowledge-graph embeddings and t-SNE place the “hybrid/local identity” between the Chinese and Portuguese clusters, acting as a bridge (silhouette ≈ 0.47). Overall, multimodal fusion is more robust than multiple baselines on recognition and semantic association tasks, revealing a resilience pathway in which Macau ICH preserves core practices while continually absorbing exogenous elements. The proposed multimodal, knowledge-driven framework effectively quantifies and explains identity (re)construction and cultural resilience in Macau’s ICH within a Sino-Portuguese milieu; the “hybrid/local identity” is the key bridging mechanism. Future work can expand cross-platform data, enhance cross-modal alignment and knowledge reasoning, and generalize the approach to other multicultural contexts to strengthen external validity.