Human resource management and competitiveness are becoming increasingly crucial in the growth of businesses. This study examines the factors of the enterprise survival environment in the process model of human asset management from the perception of control and computing. Based on systematic analysis, this study employs SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis algorithm to creatively examine the context of corresponding environmental elements on human resource management mode, and formulates a comprehensive set of human resource development plans. Simultaneously, in light of the control and computer challenges that exist in the human resource model, this study proposes specific implementation rules and methods, and theoretically verifies the efficacy and reliability of the related rules. By eliminating bias, automating tedious procedures, and empowering HR staff to make data-driven judgments, AI may expedite the hiring process. AI may save HR teams time and resources by, for instance, scheduling interviews, reviewing resumes, and performing preliminary candidate screening. It makes possible a data-driven strategy for hiring, promoting, and retaining people that aims to reduce bias and improve the experiences of both job seekers and workers. © 2024 Elsevier B.V., All rights reserved.