Deep Learning and Optimization Approaches in Attention-Based Sparse Graph Convolutional Neural Network -Based Forecast Model for Career Planning in Human Resource Management: A Review

Abstract

The rapid evolution of artificial intelligence has significantly transformed decision-making processes in Human Resource Management, particularly in career planning and workforce analytics. Among emerging methodologies, Attention-Based Sparse Graph Convolutional Neural Networks (AS-GCNNs) have demonstrated remarkable capability in modeling complex relational data inherent in organizational structures. This review paper presents a comprehensive analysis of deep learning and optimization techniques integrated within AS-GCNN frameworks for forecasting career trajectories. The study explores how attention mechanisms enhance feature relevance, while sparsity constraints improve computational efficiency and interpretability. Furthermore, optimization strategies, including metaheuristic algorithms and gradient-based tuning, are examined for their role in improving prediction accuracy and convergence stability. The paper synthesizes recent advancements, identifies research gaps, and evaluates the applicability of these models in real-world HR scenarios such as employee retention, promotion forecasting, and skill development planning. Emphasis is placed on the integration of multi-source HR data, scalability challenges, and ethical considerations in AI-driven decision systems. The findings indicate that AS-GCNN models, when combined with robust optimization techniques, offer superior predictive performance and actionable insights, positioning them as a promising tool for strategic workforce planning in modern organizations.