Human Resource Data Integration System Based on Artificial Intelligence Environment

Abstract

In an AI environment, this article suggests an HR data integration system based on a hidden semantic model to address the low integration of HR raw data. It provides a decision-making framework for enterprise personnel recruitment and employee training by making predictions and analyses based on HR information. The basis for the HR data integration model base is established in this article, along with its construction principle, process, and model types. Based on this, a method for creating an HR data integration system that has a straightforward modeling process, an easy solution, high prediction accuracy, verifiability, and correction is chosen. An HR recommendation algorithm combining a hidden semantic model and a deep forest model is proposed. At the same time, preprocess HR data and create a data warehouse. According to experiments, this system's stability can reach a maximum of 95.84 percent and its efficiency in integrating HR data can reach 96.37 percent. The system operates with ease and consistently delivers superior performance. It can more effectively realize the fusion and mining of HR data and offer practical services for related work.