Algorithm of Scenic Spot Tourism Route Planning Based on Convolution Neural Network

Abstract

Based on convolution neural network, a tourism route planning method for scenic spots is proposed. The method of performing primitive trajectory adaptive learning is used to plan and design the tourism route nodes and path space. On this basis, the shortest distribution grid structure model of tourism routes is constructed. Using the visual servo mobile route optimization control method, the constraint parameters of tourism route planning are optimized. In order to avoid the crowded area of tourists and the shortest path as the planning standard, the path planning problem is regarded as a constraint problem. The convolution neural network is used to activate the constraint function with the shortest path, and the output planning results are calculated iteratively. The simulation results show that this method has good learning control ability and convergence performance and improves the reliability of scenic spot tourism route planning.