Tourism Information Management System Using Neural Networks Driven by Particle Swarm Model

Abstract

Based on the concept of “smart tourism,” this paper designs and implements a tourism management information system based on PSO-optimized NN. The foreground tourism web page of the system adopts DIV + CSS mode for page planning and layout, PHP as the client script language, and SQL server as the database to store and analyze user information. At the same time, the system adds personalized components to the user’s search ranking results, so that the routes and scenic spots presented in front of users in the result interface are more in line with users’ consumption habits. In order to verify the performance of the model and algorithm constructed in this paper, several experiments were carried out in this paper. Experimental results show that the prediction accuracy of this algorithm is 94.67% and the recall rate is 96.11%. This algorithm can overcome the disadvantages of traditional algorithms and provide some effective suggestions for tourism management. At the same time, this paper applies the concept of “smart tourism” to specific tourism informatization, which can promote the transformation and upgrading of tourism industry structure and further enhance the overall development level of tourism industry.