Intelligent Prediction System of Sports Tourism Destination Demand Based on the Integration of Tourist Ecological Footprint Model

Jun Yue, Xianzhi Xie, Zongkeng Li, Jiaqiang Chen
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引用次数: 1

Abstract

In view of the fact that the prediction of traditional sports tourism destinations is affected by the excessive entities and large dynamic changes of economic structure, this paper proposes a method of prediction of demand intellectualization for sports tourism destinations integrating tourist ecological footprint model (TEFM). It uses tourist ecological footprint model (TEFM) to optimize the non-linear characteristic indexes that affect the demand for sports tourism destinations, and then obtain the initial data for predicting the demand for sports tourism destinations. Then it adopts the multi-objective decision-making theory to conduct trade mediation for the long-term conflict of sports tourism destinations. Finally, through TEFM it makes compensation for sports tourism destinations for long-term conflicts. The analysis of experimental results shows that compared with other different models, the model designed in this paper has high prediction accuracy and can accurately predict the demand trend for sports tourism destinations.
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基于游客生态足迹模型集成的体育旅游目的地需求智能预测系统
针对传统体育旅游目的地预测存在实体过多、经济结构动态变化大的问题,提出了一种整合旅游生态足迹模型(TEFM)的体育旅游目的地需求智能化预测方法。利用游客生态足迹模型(TEFM)对影响体育旅游目的地需求的非线性特征指标进行优化,获得预测体育旅游目的地需求的初始数据。然后运用多目标决策理论对体育旅游目的地的长期冲突进行贸易调解。最后,通过TEFM对体育旅游目的地的长期冲突进行补偿。实验结果分析表明,与其他不同模型相比,本文设计的模型具有较高的预测精度,能够准确预测体育旅游目的地的需求趋势。
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