Artificial Intelligence Technology for Satisfaction Evaluation of Tourists Under Deep Learning

In the context of accelerating digital transformation, film and television (TV) tourism, as an emerging segment, faces the dual challenges of improving tourist satisfaction and promoting sustainable economic development with the help of popular film and TV intellectual property. The main purpose of this study is to explore the application of artificial intelligence and deep learning in promoting the improvement of tourist satisfaction and sustainable economic development in the film and TV tourism field. A hybrid approach is adopted to integrate quantitative and qualitative analysis. A multivariate data model including the impact of film and TV works, tourist behavior, and tourism experience is constructed through a questionnaire, social media data analysis, and other methods. The proposed model applies deep learning-based long short-term memory to predict tourist satisfaction and optimize personalized recommendations. The results show that the model is effective in enhancing the recommendation acceptance rate and the overall satisfaction of tourists.