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.