使用深度学习模型和地理信息系统的旅游目的地的多维感知识别
Shengtian Zhang1, Yong Li1, Xiaoxia Song1
1School of Computer and Network Engineering, Shanxi Datong University, Datong, China.
PloS one
|February 7, 2025
概括
这项研究通过整合多样化的用户生成内容 (UGC) 和先进的人工智能模型来增强旅游目的地的感知. 新方法在识别目的地特征方面达到97%以上的准确性,改善了图像表示和管理.
科学领域:
- 旅游管理 旅游管理
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的旅游目的地感知对于目的地形象表示和管理至关重要.
- 以前用于目的地感知的研究方法缺乏准确性和完整性.
- 用户生成的内容 (UGC) 提供了丰富的数据,但需要复杂的分析.
研究的目的:
- 开发一个有效的策略,精确的感知识别旅游目的地.
- 确保UGC数据的完整性和感知维度的完整性.
- 为分析旅游目的地的多种看法提供一个框架.
主要方法:
- 将各种UGC数据 (图像,文本,时空信息) 集成到一个全面的数据集中.
- 员工改进了Inception V3,具有多头注意力的BiLSTM和用于特征识别的GIS.
- 开发了一种渐进的维度组合方法,用于可视化和分析感知.
主要成果:
- 在识别旅游特征信息 (内容,情感,时空维度) 方面取得了超过97%的准确性.
- 通过在中国达通的案例研究,证明了拟议战略的可行性和有效性.
- 成功分析了内容感知,情感感知和时空特征.
结论:
- 拟议的战略提供了一种可行和有效的方法来研究旅游目的地对旅游目的地的看法.
- 该方法为在目的地感知研究中选择技术路线提供了宝贵的指导.
- 这项研究通过准确的UGC分析来增强对目的地图像的理解和管理.
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