Analysis Model of Human Resource Cross-Media Fusion Based on Deep Neural Network

• Published in: Computational intelligence and neuroscience 2022-06, Vol.2022, p.1-10
• Main Authors: Ma, Shengqing, Xuan, Shanwen, Liang, Yinjing
• Format: Article
• Language: English
• Subjects: Analysis; Artificial neural networks; Bankruptcy; Communication; Decision making; Deep learning; Economic development; Economic forecasting; Equipment and supplies; Forecasts and trends; Human resource management; Information processing; Information retrieval; Linear programming; Machine learning; Management decisions; Management theory; Market economies; Market positioning; Mathematical models; Modernization; Neural networks; Resource allocation; Social environment; Strategic management; Strategic planning; Teaching
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/6069589

With the continuous deepening of enterprise system reform and the rapid development of national economy, enterprises are facing the great challenge of market competition. In the new market and social environment, the role of human resource management in enterprises becomes particularly important. To further improve the level of enterprise human resources strategic management has become an urgent problem to be solved. In the process of human resource management, enterprises are faced with complex and changeable environment and other influencing factors. Therefore, in the human resource information retrieval, this paper uses the method of deep learning to screen human resource management indicators and constructs the human resource management index system of power supply enterprises. In this paper, the nonlinear characteristics of neural network are used to establish a deep neural network human resource cross-media fusion model, which provides an operational method for enterprise human resource management. The human resource allocation relationship of enterprises is predicted, and the influencing factors and trends of personnel post-matching are analyzed. The demand forecasting results show that the neural network depth has a good fit with the enterprise staff, and the actual forecasting error is less than 3.0. It can accurately predict the human resource allocation of enterprises, improve the scientificity and effectiveness of human resource strategic decision-making, and make enterprises better adapt to the requirements of market economy. This will be of practical significance to the modernization of enterprise management.