Research on Leisure Tourism Destination Image Innovation Driven by Artificial Intelligence

As the public’s demand for tourism experience continues to improve, the demand for tourism products gradually develops in the direction of personalization, customization and differentiation. In order to mine the image of leisure tourism destinations, this paper conducts theme mining on the review texts of leisure tourism destinations by means of LDA model. An improved neural network language model CBOW and a Glove model are used to train the review word vectors of leisure tourism destination texts. Based on the deep fully connected neural network model, the perceptual features of tourist destinations are studied and analyzed. Taking Sanya tourist attractions as a research case, by analyzing the perception of tourist attractions, the cognitive image vocabulary of Sanya tourist attractions includes three major categories. Among them, tourism attractions, tourism infrastructure and services, tourism service atmosphere, and tourists’ travel behavior accounted for 53.1%, 22.3%, 5.1%, and 19.5% respectively. Then the emotional vocabulary in the perception of tourism destination image in Sanya City was categorized in terms of positive, neutral and negative, and from the neutral vocabulary, the word frequency number of words such as waiting (101), understanding (185), fun (61), and relaxation (56) was high. Regression analysis was used to explore the factors influencing the effect of tourists’ perception of destination image. Among them, tangibility (0.43), reliability (0.463), and responsiveness (0.434) were positively and significantly related to tourist satisfaction. Improving the image of leisure tourism destination can focus on these three aspects.