Application of multi-attribute decision-making combined with BERT-CNN model in the image construction of ice and snow...

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.