[目的/意义]探究多模态全息知识画像架构的构成要素，构建理论模型并阐述其运行机制，为人智交互视域下的智慧知识服务转型提供理论框架与方向指导。[方法/过程]基于人智知识交互的结构逻辑和过程逻辑，深入探究多模态全息知识画像架构的构成要素，在此基础上构建多模态全息知识画像架构的理论模型,阐释其运行机制，并剖析其应用于各领域的实践可行性与价值落地。[结果/结论]人智交互情境下，多模态全息知识画像架构的构成要素分为静态要素和动态要素两部分，静态要素包括主体、资源、空间与技术四大要素，动态要素包括需求映射、场景嵌构、记忆重构、价值重塑与服务协同五大要素；多模态全息知识画像架构可分为基础建构层、交互感知层和价值服务层三个层级；三个层级循环迭代，通过知识孪生系统建立情境化知识单元生成机制、资产化知识画像存储机制与协同化知识服务反馈机制，共同塑造了“需求—服务—反馈—优化”的一站式智慧知识服务体系，推动人智交互知识服务智慧转型升级。

[Purpose/significance] Exploring the constituent elements of a multimodal holographic knowledge profile architecture, constructing its theoretical model, and elucidating its operational mechanisms to provide a theoretical  framework and directional guidance for the transformation of intelligent knowledge services within the human-AI interaction paradigm. [Method/process] Based on the structural logic and process logic of human-AI knowledge interaction, this study delves into the constituent elements of a multimodal holographic knowledge profile architecture. Building upon this foundation, it constructs a theoretical model for the architecture, elucidates its operational mechanisms,and analyzes its practical feasibility and value realization across various application domains. [Result/conclusion] In human-AI interaction scenarios, the multimodal holographic knowledge profile architecture comprises static and dynamic components. Static elements include four core components: subject, resources, space, and technology. Dynamic elements encompass five key components: demand mapping, scenario embedding, memory reconstruction, value reshaping, and service coordination. The multimodal holographic knowledge profile architecture is structured across three hierarchical levels: the foundational construction layer, the interactive perception layer, and the value service layer. The three levels undergo cyclical iteration. Through the knowledge twin system, they establish a contextualized knowledge unit generation mechanism, an asset-based knowledge profile storage mechanism, and a collaborative knowledge service feedback mechanism. Together, they shape a one-stop intelligent knowledge service system encompassing "demand-service-feedback-optimization", driving the intelligent transformation and upgrading of human-AI interactive knowledge services.