Demand Forecast of Cold Chain Logistics of Fresh Agricultural Products Based on Deep Learning

With the continuous expansion of fresh agricultural products market and the diversification of consumer demand, the operational efficiency of cold chain logistics and the accuracy of demand forecast have become the key to the development of the industry. Aiming at the limitations of traditional forecasting methods in dealing with complex nonlinear data relations, this paper proposes a hybrid model (CNN-LSTM) combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to forecast the demand of cold chain logistics. This model uses CNN's powerful feature extraction ability to capture local features of data and uses LSTM's time series processing ability to remember historical information to predict future demand. The experimental results show that compared with the traditional forecasting methods, CNN-LSTM hybrid model is more accurate in the overall trend forecasting, especially in the peak sales season and the product types with stable demand. However, in the off-season of sales and the product types with large fluctuations in demand, the prediction error increases slightly. The research in this paper provides new ideas and methods for the accurate prediction of cold chain logistics demand of fresh agricultural products, which is helpful to improve the operational efficiency and market responsiveness of cold chain logistics.