Comparing Deep Learning Models and Information Organization Structures for Visual Content Management in Online Travel Platforms

Rapidly increasing volumes of visual content on online travel platforms require hotel photographs not only to be classified accurately but also to be presented to users within a meaningful information organization structure. Accordingly, the effectiveness of AI-enabled visual content management depends not only on the classification model used but also on the category structure adopted by the platform. This study jointly examines model selection and information organization design in the automated management of hotel images. Seven deep learning architectures were compared on a balanced dataset of 7000 hotel images under two alternative category structures, one including and one excluding the Amenities category. The models were evaluated using several performance metrics, primarily accuracy, AUC, and F1-score; class confusions and semantic overlaps were also examined through error analysis. ViT achieved the highest overall performance under both structures and reached an accuracy of 0.961, an AUC of 0.993, and an F1-score of 0.961 in the six-category structure. In the seven-category structure, which included the Amenities category, performance decreased across all models. Error analysis indicated that this decline was particularly associated with visually and semantically overlapping category boundaries. The findings indicate that, when designing AI-enabled content management for online platforms, the classification model and the information organization structure should not be evaluated independently.