For online e-commerce platforms, big data intelligent marketing is an essential tool for promoting companies, creating pictures, participating in competitions, and engaging customers. This also gives traditional marketing a new sense of precision and a new approach to increasing revenue. Marketing and public relations based on big data analytics and intelligence should be considered important and profitable for the travel and tourism business. Tourism enterprises need to learn to use intelligent technology to guide marketing activities and formulate comprehensive and accurate marketing strategies. Infiltrate the advantages of intelligence into all aspects of tourism marketing, so as to form an intelligent, precise, and modern tourism marketing and publicity model, and help enterprises to improve their income. This work focuses on the research on intelligent tourism marketing and publicity. The main research contents include the following aspects. First, this work proposes a revenue-enhancing tourism marketing and publicity method, which is mainly divided into the use of intelligence to enrich the form of publicity and marketing and the use of intelligence to refine the content of publicity and marketing. Second, this work proposes an IWOA-BP network for evaluating a revenue-enhancing intelligent tourism marketing promotion method. It improves the WOA algorithm by introducing a nonlinear convergence factor and adaptive crossover mutation to construct the IWOA algorithm. Then IWOA is applied to initial weights and thresholds for optimization, which can solve the drawbacks of the traditional BP network and improve the performance and reduce the training time. Third, this work conducts a comprehensive evaluation of the proposed revenue enhancement-oriented tourism marketing and publicity methods and IWOA-BP, and the experimental results verify the feasibility of these methods.
CITATION STYLE
Lu, Y., & Cui, B. (2022). Intelligent Tourism Marketing and Publicity Methods for Revenue Enhancement. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/6943120
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