Research on rural tourism flow density based on SVR-PSO algorithm

With the rapid development of rural tourism, accurately predicting visitor flow density has become crucial for effective resource management and enhancing visitor satisfaction. This study aims to explore the combination of Support Vector Regression (SVR) models and Particle Swarm Optimization (PSO) algorithms to analyze and forecast rural tourism flow density. By integrating SVR with PSO, we effectively optimized model parameters, improving prediction accuracy using rural tourism flow data from multiple regions and time periods for training and testing. The results indicate that the SVR-PSO algorithm significantly outperforms traditional forecasting models, providing more reliable and precise predictions. This approach not only offers feasible insights for rural tourism resource management but also enhances the overall visitor experience.