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

Abstract

With the recovery of the tourism industry and the development of the field of artificial intelligence, the application of intelligent neural network technology to management systems for safety risk assessment is the choice of the times and an urgent real need for relevant practitioners. In this study, the BP neural network algorithm is used as a tool for safety evaluation of the tourism management system. The three-layer structure of the BP neural network and the role of nodes in it are introduced, the weight values and thresholds of nodes in each of the three layers are calculated, and the particle swarm algorithm is added to optimize the model. In the practical stage, the tourism data of a place in Switzerland was selected as the training data. After 12,859 iterations, the model achieved the best calibration error of 0.126. After 300 iterations of learning, the BP algorithm optimized with the PSO algorithm has a faster convergence rate, which indicates that the performance of the optimized algorithm has improved significantly and has the global search capability that was not available before, which significantly outperforms the FastText and LSTM models. With the increase in the number of samples of tourism data, macroaccuracy always remains above 80%, so it proves that the optimization algorithm used in this study is an effective and reliable model.