Optimization Strategy of Strategic Human Resource Management Based on Big Data in Dynamic Environment

Abstract

It is very critical for enterprise human resources to be reasonably and effectively utilized, and information-based human resource management greatly improves enterprise production and work efficiency. The study combines big data algorithms to first optimize the human resource demand in HRM based on a multiple regression model, then optimize the employee performance appraisal with multiple objectives, and finally predict the employee leaving based on the SMOTE-SVM model. The SSH technology framework is combined to construct the strategic human resource management optimization system, and the TJ company is used as an example for instance analysis. After testing, the predicted value of human resource demand based on multiple regression basically matches the actual situation of the enterprise, and the maximum prediction error is only 0.55. After optimization through performance appraisal, the abnormal data decreases from 20 to 6. Employees’ marital status, stock option level, and occupational level are all key factors influencing their departure. Through the optimized human resource management optimization strategy, the enterprise can detect the employee’s status in time and bring better profits.

Keywords

• multiple regression model up
• multi-objective optimization
• smote-svm model
• ssh technique
• human resource management
• 60k37