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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Evaluation of rural tourism development level using BERT-enhanced deep learning model and BP algorithm.

Xiaohe Yuan1

  • 1Tourism and Cultural School, Development Research Center of Jilin Culture and Tourism Industry, The Tourism College of Changchun University, Changchun, 130607, Jilin province, China. yxh@tccu.edu.cn.

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Summary

This study integrates Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to improve rural tourism development assessment. The novel approach enhances accuracy by analyzing text and numerical data, supporting sustainable rural tourism.

Keywords:
AccuracyBERT-based deep learning modelBP algorithmF1 scoreRural tourism

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Sustainable Tourism

Background:

  • Traditional rural tourism development assessment methods lack sufficient expressive capabilities.
  • There is a need for more accurate and comprehensive assessment tools in rural tourism.
  • Deep learning and predictive analysis offer potential solutions for complex data assessment.

Purpose of the Study:

  • To develop and validate a novel framework for assessing rural tourism development levels.
  • To enhance the accuracy and comprehensiveness of rural tourism assessment using integrated AI models.
  • To explore the fusion of Bidirectional Encoder Representations from Transformers (BERT) and Back Propagation (BP) for multidimensional data analysis.

Main Methods:

  • Integration of the BERT deep learning model for sentiment analysis and topic extraction from textual data.
  • Application of the Back Propagation (BP) algorithm for pattern recognition and predictive analysis.
  • Collection and analysis of multidimensional rural tourism-related data, combining textual and numerical information.

Main Results:

  • The proposed BERT-BP model achieved 84.33% accuracy and 85.33% F1 score on the Laptop dataset, outperforming existing methods.
  • Ablation studies confirmed the significant contribution of BERT, BiGRU, and TextCNN components to the model's performance.
  • The integrated approach demonstrated superior ability to capture correlations between textual and numerical data, enhancing assessment credibility.

Conclusions:

  • The fusion of BERT and BP algorithms provides a powerful and accurate method for rural tourism development assessment.
  • The model's effectiveness in analyzing multidimensional data supports advancements in sustainable rural tourism.
  • This study offers significant practical and innovative value for the sustainable development of rural tourism.