Exploring Deep Learning-Powered Decision Paths for High-Quality HR Management in Private Enterprises

Abstract

High-quality decision-making in human resource management (HRM) is critical for private enterprises operating in increasingly dynamic and competitive labor markets. This paper proposes an integrated deep learning framework that exploits heterogeneous HR data-structured HRIS records, organizational graphs, temporal engagement signals, and textual resumes/job descriptions-to support three core HR decisions: employee attrition prediction, recruitment matching, and workforce forecasting. The architecture combines graph neural networks (GNNs) to model relational context, long short-term memory (LSTM) networks to capture temporal dynamics, and a transformer-based text encoder to process unstructured documents, with a shared latent fusion layer and multi-task output heads. Experiments on a combination of open HR datasets and simulated enterprise scenarios show that the proposed model outperforms traditional baselines such as logistic regression, gradient boosting, and keyword-based matching. ROC curves and error metrics demonstrate clear performance gains, while attention and feature attribution techniques provide interpretable decision paths for HR professionals. These results suggest that deep learning can support more proactive, data-driven, and explainable HR decisions in private enterprises.