Abstract: Against the backdrop of intensified global competition in technological innovation, the evaluation and management of science and technology talents have become central to national strategic planning. Traditional assessments of such talents have overemphasized quantitative indicators, neglecting the significance of tacit knowledge and individual intellectual contributions in scientific innovation activities, and thus failing to fully represent the multifaceted attributes of talents in the contemporary "post-disciplinary science" era. Beginning from the perspective of philosophy of science and technology, this study systematically analyzes the conceptual connotations and theoretical evolution of classification frameworks for science and technology talents, revealing their complexity, interdisciplinarity, and dynamic development characteristics. This provides an analytical foundation for improving the evaluation system for science and technology talents. It then focuses on the innovative applications of emerging technologies such as big data and generative artificial intelligence in the insightful analysis of science and technology talents, proposing the construction of a three-dimensional, dynamic, and individualized data portrait of science and technology talents. This portrait integrates structured data, unstructured data, and relational data, applying generative artificial intelligence to delve into the multidimensional attributes of talents, including their professional knowledge structure, innovative thinking patterns, cross-boundary influence, and developmental trajectories. Furthermore, the study explores the ecological equilibrium mechanism of cross-layer mobility of science and technology talents, emphasizing the importance of building an open, interconnected ecosystem for such talents. By analyzing the barriers and strategic mechanisms for the cross-layer mobility of science and technology talents, it proposes mechanisms for using talent portraits to promote cross-disciplinary collaboration and knowledge integration among science and technology talents, thereby facilitating the ecological equilibrium development of technological innovation.

摘要： 在面对全球科技创新竞争加剧的背景下，科技人才的评价与管理成为国家战略布局的核心。传统科技人才评价过于侧重量化指标，忽视了科技创新活动中非显性知识和个体智力贡献的重要性，难以全景展现当代“后学科科学”时代科技人才的多重属性。首先从科学技术哲学的视角出发，系统性分析了科技人才概念内涵和分类框架的理论变迁，揭示了科技人才的复杂性、交叉性和动态发展特征，为改进科技人才评价体系提供了分析基础。随后聚焦于大数据和生成式人工智能等新兴技术在科技人才洞察分析中的创新应用，提出构建立体化、动态化和个体化的科技人才数据画像，融合结构化数据、非结构化数据和关系数据，应用生成式人工智能深挖科技人才的专业知识结构、创新思维模式、跨界影响力和成长轨迹等多维属性。此外，研究探讨了科技人才跨圈层流动的生态均衡机制，强调了构建开放、互联的科技人才生态系统的重要性。通过分析科技人才跨圈层流动的障碍和策略机制，提出了运用科技人才画像促进科技人才跨界合作、知识融合的机制路径，以促进科技创新的生态均衡发展。