Long Xiaowen, Chen Weiqiang
Guilin Tourism University, Guilin, 541006, Guangxi, China.
Heliyon. 2024 Jul 5;10(14):e34159. doi: 10.1016/j.heliyon.2024.e34159. eCollection 2024 Jul 30.
In the era of sharing economy, the tourism market is increasingly characterized by personalized demand, mobile consumption and product segmentation. This paper aims to apply big data mining technology in the field of smart tourism. Firstly, it focuses on image summary selection and collaborative filtering technology based on big data mining. It then demonstrates the integration of blockchain in smart tourism, emphasizing the use of decentralized structures and smart contracts to achieve data security and transparency, and describes the testing process of smart tourism platforms, including data preparation and platform operational efficiency testing. Finally, the research results of this paper are summarized, and the development potential and practical application value of smart tourism are demonstrated. The results show that in the smart tourism big data mining model, the minimum support for the data set is 10 % and 20 %, respectively. Moreover, with the increase of the number of nodes in the same data set, the running time decreases gradually. It can be seen that smart tourism big data mining has strong scalability.
在共享经济时代，旅游市场越来越呈现出个性化需求、移动消费和产品细分的特点。本文旨在将大数据挖掘技术应用于智慧旅游领域。首先，重点关注基于大数据挖掘的图像摘要选择和协同过滤技术。接着阐述区块链在智慧旅游中的集成，强调利用去中心化结构和智能合约实现数据安全与透明，并描述智慧旅游平台的测试过程，包括数据准备和平台运营效率测试。最后，总结本文的研究成果，展示智慧旅游的发展潜力和实际应用价值。结果表明，在智慧旅游大数据挖掘模型中，对数据集的最小支持度分别为10%和20%。此外，随着同一数据集中节点数量的增加，运行时间逐渐减少。可见，智慧旅游大数据挖掘具有很强的可扩展性。
