Forecast and Application of GA Optimization BP Neural Network Tourism Demand in High-speed Railway Era

Abstract

The arrival of the high-speed rail era has profoundly affected China’s tourism demand, making it towards uncertainty, and traditional tourism demand forecasting methods need to be innovated. Using Matlab (2014a) to construct a BP neural network model based on genetic algorithm (GA) optimization, taking the number of tourists and tourism income of Sanjiang Dong Autonomous County in Guangxi as sample data, the model is trained repeatedly, and the number of tourists and tourism income are predicted and analyzed, and the model is applied to the forecast of tourism demand in the era of high-speed railway. The example simulation results show that the GA-optimized BP neural network model in the high-speed rail era has better adaptability and prediction accuracy in tourism demand.