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. 2022 Jun 10:2022:6069589.
doi: 10.1155/2022/6069589. eCollection 2022.

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

Shengqing Ma  1 , Shanwen Xuan  2 , Yinjing Liang  3
Affiliations

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

Shengqing Ma et al. Comput Intell Neurosci. .

Abstract

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.

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Conflict of interest statement

The authors declare that they have no conflicts of interest or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Neural network training error display diagram.
Figure 2
Figure 2
Frame diagram of human resource cross-media convergence based on deep neural network.
Figure 3
Figure 3
Cross-media deep learning implementation process design diagram.
Figure 4
Figure 4
Fuzzy inference model of social security fund audit.
Figure 5
Figure 5
Matrix result diagram of the neural network fusion model.
Figure 6
Figure 6
The forecast results of enterprise human resource structure from 2011 to 2021.
Figure 7
Figure 7
Risk output value and expert evaluation score value are not displayed as test results.
See this image and copyright information in PMC

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