Evaluation of Cross Border E-Commerce Talent Cultivation based on Back Propagation Neural Network

From the past few years, cross-border e-commerce has rapidly expanded with globalization and digital transformation which requires a skilled people to monitor international trade complexities, digital marketing, and cross-cultural business interactions. Nevertheless, talent cultivation faces difficulties such as curriculum gaps, insufficient practical training, and a lack of alignment with industry demands. Even though previous researchers suggested effective models still they fail to integrate evolving e-commerce trends, technological advancements, and global trade regulations etc. Therefore, this research proposes an effective model using Genetic Algorithm with Back Propagation Neural Network (GA-BPNN). Initially, data collected from human resource dataset which contains candidate resumes, job descriptions, and historical recruitment records. Then, the collected data is preprocessed using regression imputation and modified Z-score normalization for handling missing values and data standardization respectively. Furthermore, BPNN is employed for evaluation and GA is employed for optimizing the performance of BPNN. The proposed GA-BPNN established better results including accuracy (8.98%) when compared to existing Convolutional Neural Network (CNN).