BP neural network based on Qingdao City air logistics demand forecast

BP neural network is a kind of back-propagation artificial neural network, which can achieve simulation and prediction of complex systems through learning and training. Its powerful adaptive and self-learning capabilities enable it to play an important role in solving the prediction problem of air logistics industry. In order to predict the scale of air logistics demand in Qingdao more accurately, this paper combines theoretical analysis and empirical research to build a BP neural network prediction model. The results show that the scale of air logistics demand in Qingdao will show a steady growth in the next three years. The model can accurately predict the future trend and provide theoretical basis for the decision of enterprises and government. The study shows that the BP neural network prediction model is optimized through error analysis, and the prediction accuracy is high, and it is successfully applied to the prediction of air logistics demand, which can provide theoretical support for the planning of relevant departments.