As technological advancements in AI unfold

advanced machine learning techniques are increasingly utilized within logistics operations. logistics demand forecasting

as a core task in logistics management is shaped by multiple influencing factors

including weather as well as economic conditions

showing intricate spatiotemporal patterns. To address this

a fusion model integrating CNNs and LSTMs is proposed for enhanced performance in this study to improve the precision of logistics demand prediction. During this model

input data is processed by CNN to obtain relevant features

while LSTM is employed to learn and predict the extracted feature data

ultimately yielding the final prediction results. Public freight volume data from Beijing is selected as experimental data

and the forecasting model is implemented using Python. Comparative experiments with standalone CNN and LSTM models show that

compared to single deep learning architectures

the CNN-LSTM hybrid model demonstrates more stable and reliable performance in multi-dimensional and dynamic logistics demand forecasting. The study indicates that logistics demand forecasting based on this model can provide more accurate demand predictions for logistics companies

thereby optimizing resource allocation

reducing operational costs

and offering broad application prospects.