With the continuous advancement in technology, the tourism sector has developed to become one of the most important sectors globally in the modern period. These factors have given rise to the concept of “smart tourism,” which can be described as a step forward from conventional tourism. To achieve a true smart tourism experience, the appropriate services must be supplied to the correct user at the right time and in the most efficient and feasible manner. Keeping the importance of technology and tourism in consideration, the proposal and penetration of innovation-driven development strategies and smart tourism have become the focus of attention in the tourism industry at this stage. Smart tourism is comprised of a large number of tourists, devices, and operational processes which generate an enormous volume of tourism data. Handling such large amounts of tourism data in an effective and accurate manner is indeed an important thing to consider. To handle this issue, this study focuses on the construction and design of a smart tourism model based on big data technologies. This study explains in detail the relationship between smart tourism and big data and explores the construction of smart tourism applications in the context of big data. By explaining the relationship between smart tourism and big data, it is pointed out that the development of smart tourism needs to rely on the construction of a smart tourism application model under the background of big data. To this end, the role of smart tourism, building smart tourism platforms, improving information sharing mechanisms, exploring the implementation path of smart tourism application models, and further promoting the development of smart tourism, is of a great interest for the enterprises, scholars, and tourists. The proposed model is expected to be of a great help for the tourism industry.

Aiming at the defect that the click-through rate of marketing advertisements cannot provide accurate prediction results for the company in time in the marketing strategy of Internet companies, this paper uses a deep learning algorithm to establish a prediction model for the click-through rate of marketing advertisements. The suggested model is called high-order cross-feature network (HCN). Furthermore, this paper also introduces the combination of feature vectors into the graph structure and as the nodes in the graph; therefore, the graph neural network (GNN) is used to obtain the high-level representation ability of structured data more fully. Through numerical simulations, we observed that HCN has the capability to provide Internet companies with more accurate advertising business information, user information, and advertising content. Moreover, HCN model is more reasonable to adjust the advertising strategy and can provide better user experience. The simulation outcomes indicate that the suggested HCN approach has noble adaptability and high correctness in forecasting the click-through rate of marketing advertisements. We observed that this improvement, in terms of predictions precisions and accuracies, can be as high as 17.52% higher than the deep neural network (DNN) method and 10.45% higher than the factorization network (FM) approach.