Construction of an Intelligent Archive System for Cross border E-commerce Talent Management Based on Artificial Intelligence

This study aims to build an innovative talent management system in the field of cross-border e-commerce. In view of the challenges faced by cross-border e-commerce in talent management, we chose the Convolutional Neural Network (CNN) model to develop our system. The purpose of this choice is to manage talent information more efficiently, optimize talent management processes, and improve the efficiency and quality of talent decision-making. In the experimental stage of this article, four experiments were conducted to verify the effectiveness of the research system in improving the efficiency of talent management and decision-making quality of cross-border e-commerce enterprises. In the accuracy evaluation experiment, the CNN model obtained an AUC score of 0.92 in the accuracy test. In the second matching efficiency evaluation experiment, the processing time based on the CNN model only required an average of 0.75 seconds. In the generalization ability evaluation experiment, the accuracy of the CNN model in the fields of IT, finance, and education were 0.92, 0.89, and 0.91, respectively. From the above data conclusions, it can be seen that although each model has its unique advantages, the CNN model performs the best in multiple key dimensions. These findings provide some good suggestions for the research platform when selecting talent matching algorithms.