基于BP神经网络的农产品跨境电商人才培养方案设计与应用

Design and Application of a Cross-border E-commerce Talent Training Scheme for Agricultural Products Based on BP Neural Network

在经过广泛调研专业农产品跨境电商人才的知识体系和技能的基础上,总结分析专业农产品跨境电商人才具备的9大重要特征。利用这些特征量化收集得到的652份样本数据,建立基于BP神经网络的农产品跨境电商人才培养算法模型。实际应用结果表明,采用十字交叉验证方法进行验证,分类的准确度达到了95%,相比传统的KNN分类算法,分类精确度较高,节省了评估农产品跨境电商人才具有不同水准的人力物力,同时可以针对不同类别的跨境电商人才,提供精准培养,避免资源浪费。

Based on extensive research on the knowledge system and skills of adhesive cross-border e-commerce talents,this article summarizes and analyzes nine important characteristics of cross-border e-commerce talents for chemical products.These characteristics are used to quantify the 652 sample data collected,and an adhesive cross-border electric business talent training algorithm model based on BP neural network is established.The actual application results show that the cross-validation method can be used for verification,and the classification accuracy reaches 95%.Compared with the traditional KNN classification algorithm,the classification accuracy is higher,which saves the evaluation of adhesive cross-border electricity talents to have different levels.The manpower and material resources can also provide accurate training for different types of cross-border e-commerce talents and avoid waste of resources.