Cross-border e-commerce supply chain optimisation with transfer learning models

To solve the problems of data sparsity, unstable demand and multi-stage coordination in the cross-border e-commerce supply chain optimisation bottleneck, this paper puts forward an end-to-end decision-making framework based on multi-source hierarchical transfer learning and deep reinforcement learning.By performing multi-source adversarial adaptation in feature space to learn domain-invariant representations and introducing a meta-learning weighting mechanism at the sample level to refine the selection of beneficial knowledge, this approach systematically mitigates model cold-start and negative transfer issues.Experiment shows that the framework greatly improves the forecast accuracy of demand from 4.83 to 5.76 compared to the basic model.In simulated cross-border supply chain networks, it cuts down the overall operation expenses by 7.2%, and shortens the time taken to fulfil orders by 13.5%.