Deep Learning Algorithm Based Smart Tourism: Addressing Role, Challenges and Research Gaps in Exploration and Narration

This research explores how Artificial Intelligence can transform the tourism industry through classification techniques and Natural Language Processing models. Artificial Intelligence based classification methods such as Support Vector Machines, Convolutional Neural Networks, and lightweight models like SqueezeNet are being used to recognize tourist destinations, monuments, and landmarks. These techniques support applications such as automated spot identification, real-time landmark detection, clustering of attractions, and mobile-based travel assistance. Natural Language Processing based generative models, including GPT, BERT, and BART are used for creating travel narratives, providing instant language translation, generating summaries and supporting intelligent itinerary planning. All these methods improve personalization, enhance tourist engagement and help travel agencies deliver efficient services. Also the classification models face issues like limited datasets, image variations, computational demands and poor scalability. Generative text models struggle with accuracy, multilingual limitations, high resource requirements, and ethical concerns such as privacy and bias. Current research also shows a gap: most studies treat image recognition and text generation separately, leading to less immersive tourist experiences. This study highlights the need for integrated Deep Learning systems that can provide tourists with accurate place recognition and narratives (stories) for smart tourism exploration.