Application of grey BP neural network in port logistics demand analysis

From the perspective of port cargo throughput, this paper firstly analyses the characteristics and influencing factors of port logistics demand. Secondly, considering the characteristics of nonlinear logistics demand and small sample modelling, the modelling adopts GM(1, 1) and the single prediction model of BP neural network for calculation. Then, based on the prediction results and the target of minimum fitting prediction square-error, the single model is given weight, and the combined prediction model is constructed. Finally, taken Qingdao Port as an example, the port logistics demand is simulated by MATLAB software. The results show that the combined forecasting model has higher accuracy and stronger stability than the single forecasting model, which can effectively reduce the error rate and make the forecasting result closer to reality, thus having guiding significance for the future port logistics development planning.