Simulation of Virtual Market Model of Tourism Industry Based on Generative Adversarial Networks Algorithm

This study explores the feasibility and effectiveness of applying Generative Adversarial Networks (GAN) algorithm to simulate virtual market models in the tourism industry. Against the backdrop of sustained global economic growth and booming tourism industry, the complexity and variability of the tourism market pose challenges to policy formulation, market analysis, and strategy formulation. In order to predict the market dynamics more accurately and optimize the allocation of resources, this study uses GAN algorithm to build a virtual market model of tourism industry. By introducing the structure of Conditional Generative Adversarial Networks (cGAN), the model can receive additional conditional information, such as seasons, holidays, etc., thus generating market data matching specific conditions. The experimental results show that the cGAN model can successfully simulate the price fluctuation, consumer behavior and market trend of the tourism market, and provide an accurate forecast of tourism sales and consumer purchase decisions. In addition, cGAN model also reveals the comprehensive influence of seasons, holidays, promotional activities and economic policies on tourism sales.