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<p>As an emerging industry that deeply integrates tourism and sports industries, sports tourism provides a practical platform for cultivating high-quality and professional talents with multidisciplinary backgrounds in leisure sports, tourism management, information technology, and other fields, enhances cross-disciplinary collaboration, facilitates cultural heritage, and improves the overall quality of tourism talents. This article constructs an evaluation model through deep learning technology, extracts the characteristics of various influencing factors from the needs of tourism talent training, the characteristics of sports tourism, and the characteristics of sports tourism scenarios, and uses the typical convolutional neural network VGGNet model to construct a feature extraction and classification model. It analyzes factors such as the knowledge level, theoretical level, work performance, and work attitude of talents, and establishes a decision-making system for tourism talent training. Finally, by optimizing and establishing convolutional kernel CNN Bi-GRU,the attention mechanism model, combined with case analysis and calculation, obtains comprehensive indicators for evaluating tourism talents, and uses empirical analysis to identify optimization strategies needed for the tourism talent training mechanism. It also reflects the positive role of the sports tourism environment in the cultivation of tourism talents, which can indirectly influence the direction and demand for tourism talent training. Compared with traditional tourism talent evaluation methods, the core innovation of this study lies in integrating deep learning technology (VGGNet, CNN-Bi-GRU attention mechanism model) into the talent evaluation system, realizing intelligent, quantitative extraction of talent evaluation indicators, and breaking the limitations of traditional manual evaluation and single-dimensional index evaluation, which can more scientifically and comprehensively reflect the comprehensive quality of tourism talents and provide more targeted support for the optimization of talent training mechanisms.</p>

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The role of sports tourism in promoting talent cultivation in the tourism industry with research on the construction of evaluation models based on deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Huan Tang,
  • Fateh Zereg,
  • Yifan Chen,
  • Siqi Xie

摘要

As an emerging industry that deeply integrates tourism and sports industries, sports tourism provides a practical platform for cultivating high-quality and professional talents with multidisciplinary backgrounds in leisure sports, tourism management, information technology, and other fields, enhances cross-disciplinary collaboration, facilitates cultural heritage, and improves the overall quality of tourism talents. This article constructs an evaluation model through deep learning technology, extracts the characteristics of various influencing factors from the needs of tourism talent training, the characteristics of sports tourism, and the characteristics of sports tourism scenarios, and uses the typical convolutional neural network VGGNet model to construct a feature extraction and classification model. It analyzes factors such as the knowledge level, theoretical level, work performance, and work attitude of talents, and establishes a decision-making system for tourism talent training. Finally, by optimizing and establishing convolutional kernel CNN Bi-GRU,the attention mechanism model, combined with case analysis and calculation, obtains comprehensive indicators for evaluating tourism talents, and uses empirical analysis to identify optimization strategies needed for the tourism talent training mechanism. It also reflects the positive role of the sports tourism environment in the cultivation of tourism talents, which can indirectly influence the direction and demand for tourism talent training. Compared with traditional tourism talent evaluation methods, the core innovation of this study lies in integrating deep learning technology (VGGNet, CNN-Bi-GRU attention mechanism model) into the talent evaluation system, realizing intelligent, quantitative extraction of talent evaluation indicators, and breaking the limitations of traditional manual evaluation and single-dimensional index evaluation, which can more scientifically and comprehensively reflect the comprehensive quality of tourism talents and provide more targeted support for the optimization of talent training mechanisms.