Application of NLP and Deep Learning in Enhancing the Resilience of Cross border E-commerce Supply Chain for Agricultural Products

Agricultural products have time constraints and logistics transportation cost constraints, which reduce the elasticity of the supply chain. In order to combine market trends, reasonably control supply volume, and improve supply efficiency, an application method of NLP and deep learning is proposed to enhance the elasticity of the cross-border e-commerce supply chain for agricultural products. Using deep learning algorithm BP neural network to predict market demand for agricultural cross-border e-commerce; Using NLP technology to monitor and analyze textual information in the cross-border e-commerce supply chain of agricultural products, and utilizing deep learning models to identify potential risk factors that may affect the supply chain; Apply the word frequency inverse document frequency algorithm and Word2Vec algorithm from NLP technology to multi feature extraction of logistics transportation text. A logistics transportation optimization model for agricultural cross-border e-commerce supply chain considering potential risk factors was constructed by combining deep learning. Adopting enhanced particle swarm optimization algorithm to solve the model, matching multiple features, and achieving optimization of agricultural cross-border e-commerce supply chain logistics transportation. The experimental results show that this method has a lower frequency of supply chain interruptions and a lower error rate in supply chain demand forecasting after application. By integrating NLP and deep learning methods, logistics transportation costs can be effectively reduced, supply chain resilience can be enhanced, and it has good practical application effects.