Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 1980037
• Main Author: Guo, Wenru
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Back propagation; Back propagation networks; Correlation analysis; Entropy; Errors; Evaluation; Expected values; Fuzzy sets; Information sharing; Management; Neural networks; Neural Networks, Computer; Optimization algorithms; Radial basis function; Risk Assessment; Security; Security software; Tourism; Travel industry; Travelers; Velocity
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2021/1980037

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.