Hierarchical Multi-label Classification Model Research for Scenic Area Tourism Resources

Research on tourism resource demand offers critical decision support for the protection, development, and marketing of tourism resources. It also improves personalized experiences and satisfaction for tourists, thus fostering the advancement of the tourism industry into broader and higher realms. It also improves personalized experiences and satisfaction for tourists, thus fostering the advancement of the tourism industry into broader and higher realms. The objective of this study is to extract characteristics of 3A-level and above scenic spots nationwide, facilitating the integration and utilization of tourism resources in the country. The objective of this study is to extract characteristics of 3A-level and above scenic spots nationwide, facilitating the integration and utilization of tourism information. This facilitates the automation and efficiency of tourism resource classification and provides suggestions for improving services at these scenic areas. In response to the abundant resources of major scenic areas across the country, a new approach has been developed. In response to the abundant resources of major scenic areas across the country, this study introduces a hierarchical multi-label classification model for tourism resources. tourism resource classification theme system issued by the Ministry of Culture and Tourism, and leveraging FastText for pre-training on scenic area introductory texts, this research combines the advantages of the hierarchical multi-label classification model with the theme system of the Ministry of Culture and Tourism. introductory texts, this research combines the traditional LSTM model with an attention-based Transformer model. Additionally, a Graph-Convolutional Network (GCN) is used to classify tourism resources. Convolutional Network (GCN) is employed as a hierarchical structure-aware encoder to construct the MiLCT, a hierarchical multi-label classification model, enabling sophisticated multi-label classification to be applied to the text. model, enabling sophisticated multi-label classification of tourism scenic area resource data. Experimental comparisons demonstrate that, with increasing classification levels, the proposed model outperforms those lacking GCN and Transformer components in terms of micro-Precision, micro-Recision, micro-Recision, and micro-Recision. micro-Precision, micro-Recall, and micro-F1 scores, this indicates that the hierarchical structural information of the model can significantly enhance its performance. In comparison to the hierarchical multi-label correlation model, the proposed model demonstrates enhanced performance across the evaluated metrics, indicating its efficacy in integrating multimodal features and providing more comprehensive and accurate data characterization.