Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning

Abstract

Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.