A Study on Digital Transformation Models for Corporate Human Resource Management Based on Big Data Analysis

Abstract

In the era of digital economy, corporate human resource management faces significant challenges in transformation and upgrading. This paper proposes a novel computational framework for HR digital transformation leveraging big data analytics and deep learning technologies. The core technical contributions include: (1) A distributed Lambda architecture-based data processing pipeline that handles both batch and stream processing of massive HR data; (2) An ensemble learning model combining XGBoost and LightGBM algorithms for talent analytics and prediction; (3) A knowledge graph-based decision support system utilizing BiLSTM-CRF for entity recognition and TransE for knowledge representation. The implemented platform achieves 91% accuracy in talent assessment and reduces decision latency to under 3 seconds. Experimental results demonstrate significant improvements in recruitment efficiency and talent retention, providing a practical reference for enterprise HR digital transformation through advanced computing technologies.