The traditional E-government big data system fills and classifies algorithms with low accuracy and poor work efficiency. With the development and wide application of big data, the internet of things, and other technologies, the integration of information resources has become the key to information construction. In the process of information resource integration, there are still outstanding problems such as incomplete government information resource system, different standards of government information resource management system construction, and serious threats to network and information security. In order to solve this problem, a new E-government big data system filling and classification algorithm is studied in the cloud computing environment; E-government big data filling is carried out on the basis of complete compatibility theory; and the E-government big data computing intelligence system in the cloud computing environment is constructed and its development impact, so as to parallelize the data, classify the data through decision trees, and realize incremental update decision forest parallelization processing. To verify the effectiveness of the method, comparative experiments are set, and the results demonstrate that experiment one is randomly built into the classification model, and according to the decision forest algorithm, the optimal number of decision trees is 24.
Existing research has established a link between leader–member exchange (LMX) and employee voice. However, there is still a wide scope for exploring the mechanisms of this relationship. From the perspective of traditional Chinese values, we investigated the mediating role of Zhongyong thinking in the relationship between LMX and employee voice. We conducted a field survey with 252 employees of a state-owned organization in China. Results of structural equation modeling show there was a significant and positive relationship between LMX and employee voice. Additionally, Zhongyong thinking was an important mediating mechanism. This study indicates voice behavior can be fostered by establishing a high-quality LMX relationship and cultivating Zhongyong thinking among employees. Suggestions for future research are offered.
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.
The purpose of university personnel management is to improve the efficiency of running a school by optimizing the allocation of university human resources. Personnel management is one of the most important aspects of university administration, as it plays a key role in the formation of discipline teams and management teams. The use of advanced information technology is to improve the efficiency of personnel management, the establishment of university human resources analysis system, and to promote the school management work. Combining with the achievements of office automation and decision support development, this study analyzes the realization methods and key technologies of human resource management in university decision system, discusses the significance of human resource management in university decision system analysis based on decision model, and constructs human resource management in university decision system. This study first introduces the basic content of human resource management decision model and makes a comprehensive analysis of human resource demand prediction and then constructs a human resource management in university decision model according to the analysis structure. After defining the construction path of the decision-making model of human resource management in colleges and universities, the human resource grading and promotion analysis is carried out to additionally define the prediction of human resource demand in universities. Finally, the decision system is realized and tested by means of mathematical model.
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