A Particle Swarm Optimization Based Neural Network Model for Tourism Machination System

Tourism informatization has become a critical driver of global tourism growth and a competitive advantage in resource allocation. As one of the world’s major industries, alongside automobiles and oil, tourism is rapidly expanding with the improvement of living standards. However, this growth has also introduced challenges such as inefficiency, errors in data management, and outdated administrative practices. To address these issues, the development of a comprehensive tourism management information system is essential to meet evolving market demands and improve efficiency. This paper presents a “smart tourism” approach by leveraging advanced technologies, including Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO), to enhance tourism management. The study introduces a novel tourism management system based on a PSO hybridized Back Propagation Neural Network (BPNN), addressing inherent limitations of traditional BPNN such as slow convergence, susceptibility to local minima, and oscillation. Key innovations include a price-comparison decision method to optimize tourist route selection and ensure secure, reliable system functionality through rigorous testing. Experimental results demonstrate the improved efficiency and reliability of the PSO-optimized system, highlighting its potential to address critical challenges in tourism management and promote further innovation in the field. Our proposed model’s prediction accuracy, according to experimental results, is 94.67% and furthermore, 96.11% is the recovery rate.