Jin Hua
School of International Culture and Tourism, Jilin International Studies University, Changchun, 130000, China.
Sci Rep. 2025 Mar 27;15(1):10613. doi: 10.1038/s41598-025-95221-5.
This study proposes an innovative evaluation framework that integrates deep learning with multi-attribute decision-making (MADM) methods to enhance the scientific rigor and accuracy of image evaluation of ice and snow tourism destinations. Compared to traditional evaluation approaches, this framework effectively processes unstructured textual data and conducts comprehensive assessments across multiple dimensions. The study innovatively designs a text feature extraction model based on the Bidirectional Encoder Representations from Transformers (BERT)-Convolutional Neural Network (CNN). Meanwhile, MADM methods are introduced for attribute weight allocation and decision optimization. The model employs BERT for in-depth semantic analysis of tourist reviews, utilizes CNN to extract local textual features, and combines MADM methods to generate comprehensive scores. In the study, the optimized model demonstrates a high consistency, achieving a consistency ratio of only 0.03 in the facilities and services theme. Moreover, this model significantly outperforms the Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa), with a consistency ratio of 0.06. Regarding priority stability, the optimized model reaches 0.91 in comprehensive experience themes. In the aspect of computing time, the inference time of the optimized model is 0.14 s in the facilities and services theme. The experimental results indicate that the optimized model performs well in dealing with complex unstructured text data while showing high efficiency and stability in weight allocation and multidimensional decision-making tasks. Therefore, this study contributes meaningfully to the research in the image evaluation field for ice and snow tourism destinations. It also provides a vital theoretical basis and practical tools for tourism image optimization, precise marketing, and scientific management.
本研究提出了一种创新的评估框架，该框架将深度学习与多属性决策（MADM）方法相结合，以提高冰雪旅游目的地图像评估的科学严谨性和准确性。与传统评估方法相比，该框架能有效处理非结构化文本数据并进行多维度综合评估。本研究创新性地设计了一种基于双向编码器表征变换器（BERT）-卷积神经网络（CNN）的文本特征提取模型。同时，引入MADM方法进行属性权重分配和决策优化。该模型利用BERT对游客评论进行深入语义分析，利用CNN提取局部文本特征，并结合MADM方法生成综合得分。在研究中，优化后的模型表现出高度一致性，在设施与服务主题上的一致性比率仅为0.03。此外，该模型显著优于基于变换器的稳健优化双向编码器表征方法（RoBERTa），其一致性比率为0.06。在优先级稳定性方面，优化后的模型在综合体验主题上达到0.91。在计算时间方面，优化后的模型在设施与服务主题上的推理时间为0.14秒。实验结果表明，优化后的模型在处理复杂的非结构化文本数据方面表现良好，同时在权重分配和多维度决策任务中表现出高效率和稳定性。因此，本研究对冰雪旅游目的地图像评估领域的研究做出了有意义的贡献。它还为旅游形象优化、精准营销和科学管理提供了重要的理论基础和实用工具。
