DIKWP-Driven Purpose Lifecycle Modeling and Semantic Path Construction

Purpose-driven Artificial Intelligence (AI) systems must exhibit adaptive purpose perception, dynamic adjustments, and multi-level feedback when operated in complex and evolving environments. However, traditional AI models lack a unified mechanism for modeling purpose lifecycle, thereby resulting in challenges in behavior traceability, control, and optimization, which, in turn, limit interpretability and long-term effectiveness. This paper proposes a Data-Information-Knowledge-Wisdom-Purpose (DIKWP)-based semantic framework for purpose lifecycle management oriented toward cognitive evolutionary pathways. This mechanism consists of five semantic stages: data-layer dynamic verification, information-layer migration response, knowledge-layer logical reconstruction, wisdom-layer value evolution, and purpose-layer goal closure and conflict regulation. A multi-level, multi-goal, and multi-feedback semantic governance structure is formed. In addition, multi-layer graph modeling and cognitive space differentiation are introduced, specifically between conceptual and semantic spaces, to enable structured and visual modeling of purpose generation, updating, and tuning. By integrating the dual-loop structure of "experience-narrative" from artificial consciousness theory, the purpose stability and adaptability of the system in interactive environments are enhanced. The proposed mechanism was theoretically validated in smart home and smart city scenarios. The experimental results demonstrate its generality, scalability, and robustness, offering theoretical and engineering support for value alignment, semantic safety, and autonomous evolution in sovereign AI systems.