An Evaluation Model of Comprehensive Human Resources Quality of Financial Enterprises Based on Deep Neural Network
Yongda Hu, Xiyuan Li · Ingénierie des systèmes d information · 2020
The comprehensive evaluation of human resources (HR) quality is of greatly significance to improving the economic efficiency of financial enterprises.Based on deep neural network (DNN), this paper mainly proposes an evaluation model of comprehensive HR quality of financial enterprises, which dynamically identifies the HR quality that matches the posts at different layers.Firstly, a reasonable evaluation index system (EIS) was established, including 5 primary indices and 21 secondary indices.The evaluation problem was decomposed into multiple layers and indices.On this basis, an N-index convolutional neural network (CNN), i.e. the N-evaluation model, was established based on the Nevaluation model, which takes the improvement of the comprehensive HR quality into consideration.Finally, experiments were conducted to verify the effectiveness of the proposed model.The research results provide reference for the application of DNN in other evaluation fields.