Tourism Planning Path Based on Ant Colony Optimization Algorithm

The development of tourism has led to the development of related industries. For the personalized travel recommendation platform, to be closer to the needs of users, we analyze the tourist data stored in the platform. We also select the basis of user behavior analysis to explore the quantitative relationship between database data and real-time information on tourist attractions, which is to guide and design personalized travel routes for tourists. However, the current personalized recommendation system has a low level of technology, and most of them are based on static data as external features, which can not meet the real-time needs of users. In this paper, for the traditional prediction navigation scheme of the optimal solution of straight-line distance, the real-time dynamic prediction analysis of user interest which is transformed into the optimal solution of time is innovatively used. The planning scheme is calculated by combining the relevant indicators of tourist attractions. The heuristic factor of the improved ant colony algorithm is adopted to calculate the travel path. The Dijkstra least square method is applied to solve the pheromone update law to customize the route planning for tourists during their travel. The simulation results indicate that the least square method of the optimal solution of the time trajectory has technical advantages in the tourism planning. It provides technical support for the individualized planning of tourism industry and contributes to the traditional navigation trajectory prediction research.