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“Internet + Artificial Intelligence” Human Resource Information Management System Construction Innovation and Research

Author

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  • Zhen Zeng
  • Longqi Qi

Abstract

Human resources are the cornerstone of operational operation. Good management of human resource information can enable businesses to operate effectively. However, for the time being, most enterprises still use traditional methods of allocating human resources, which are difficult to meet the development needs of enterprises. In order to find the optimal allocation of human resources in an enterprise, this article is based on “Internet + artificial intelligence,” using methods such as case analysis and literature analysis to collect data from databases such as CNKI, Wanfang Database, and SSCI, and uses fog computing to build a model for the optimal allocation of human resources which is proposed, and a large number of relevant literature studies are read and analyzed through the literature survey method. According to the research needs, through the research and summary of the content of the literature, the research structure found that there are many problems in the current human resource information management system. After the optimization of models and algorithms, the allocation of human resources has been greatly improved. The matching rate of personnel and job positions has increased by more than 50%. The operation efficiency index of the enterprise is above 0.8, an increase of about 30%. This shows that “Internet + artificial intelligence” can effectively optimize the enterprise human resource information management system, promote the greater use of its human resource value, bring higher economic returns to the enterprise, and provide assistance for the long-term stable and healthy development of the enterprise.

Suggested Citation

  • Zhen Zeng & Longqi Qi, 2021. "“Internet + Artificial Intelligence” Human Resource Information Management System Construction Innovation and Research," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, March.
  • Handle: RePEc:hin:jnlmpe:5585753
    DOI: 10.1155/2021/5585753
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