在旅游教育中的物联网驱动的BP预测模型的应用和优化
1School of Tourism Culture, The Tourism College of Changchun University, Changchun, 130607, China. lvq@tccu.edu.cn.
Scientific reports
|April 26, 2025
概括
这项研究优化了行业和学校在旅游教育方面的合作,使用了反向传播 (BP) 预测模型. 增强的BP模型显著提高了人才培养的准确性和速度,优于传统方法.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 旅游管理 旅游管理
背景情况:
- 产业与学校之间的合作对于培养旅游教育中的人才至关重要.
- 物联网 (IoT) 为教育模式带来了新的机遇和挑战.
- 传统的人才培养模式可能无法充分利用技术进步.
研究的目的:
- 在物联网框架内调查旅游学校的行业-学校合作和人才培养模式.
- 应用和优化旅游教育的反向传播 (BP) 预测模型.
- 评估优化BP模型与传统方法的有效性.
主要方法:
- 使用反向传播 (BP) 预测模型来分析行业-学校合作和人才培养.
- 为旅游教育和物联网环境的特定环境优化了BP模型.
- 优化BP模型的性能与支持矢量机 (SVM) 和随机森林 (RF) 模型进行了比较.
主要成果:
- 与SVM和RF相比,优化的BP模型表现出卓越的准确性 (86.2%-88.7%) 和显著更快的预测时间 (1000条预测为0.05秒).
- 在文本,数值和分类数据类型中实现了较低的平均平方误差 (0.059-0.067).
- 实验结果验证了该模型的有效性和与传统方法相比的实质优势.
结论:
- 建议加强行业与学校的合作,扩大合作伙伴关系,增加实习机会.
- 优化的BP模型为提高旅游教育中人才培养策略提供了有价值的指导.
- 这项研究促进了BP预测模型在教育中的跨学科应用,促进了实践技能和就业能力.
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