Explainable AI with fine-tuned large language models for sustainable cultural heritage management

The redevelopment of cultural heritage areas, especially in historical urban environments, requires a nuanced understanding of public perceptions to balance preservation with modernization. This study introduces an advanced AI-driven framework for Aspect Sentiment Quadruple Prediction (ASQP) to assess public perceptions of Lijiang Ancient Town, a UNESCO World Heritage site in China. We fine-tuned the large language model Qwen-14B using LoRA-based methods to augment sentiment data, effectively uncovering implicit emotional cues in social media content. The model integrates BERT, multi-layer BiLSTM, self-attention, CNN, and CRF for enhanced entity recognition and sentiment classification. Experimental results show that the enhanced model (Qwen-14B + ASQP) improved F1-score by 0.97% (from 75.42% to 76.39%) and Precision by 4.48% (from 76.14% to 80.62%) compared to the baseline. Analyzing data from platforms such as Weibo, Dazhong Dianping, and Xiaohongshu (2018-2024), the research uncovers factors influencing public perception, offering insights for heritage site management, urban planning, and the sustainable preservation of cultural heritage.