在大数据蓬勃发展的时代背景下，可信数据空间这一数据要素高效流通的关键基础设施应运而生。然而，当前的可信数据空间构建普遍陷入碎片化与静态化困境，其重准入、轻流转、缺追溯的断点式信任模式，使其在内部处理与融合增值时易发生隐私泄露、隐私篡改以及跨域互信缺失等风险，成为制约可信数据空间以及数据要素市场高质量发展的掣肘。全生命周期理论强调数据处理的动态演化与闭环治理，为可信数据空间的构建提供了系统性的理论参考。具体而言，就是在整合各类可信数据空间的“分级”“分类”的结构化互联与全生命周期的动态信任演化前提下，提出可信数据空间应在前端严守准入信任，中端保障过程信任和末端实现结果信任，并完善全流程信任管理体系的构建范式，从而提升数据空间的可持续性、适应性，推动数字经济的高质量发展。

Abstract In the era of big data, Trusted Data Spaces serve as critical infrastructure for the efficient circulation of data elements. However, the current construction of these spaces is often trapped in a predicament of fragmentation and staticity. This results in a discontinuous trust model that overemphasizes initial admission while neglecting in-process circulation and ex-post traceability. Consequently, persistent risks such as privacy breaches, privacy tampering, and a lack of cross-domain trust during internal processing and value-added integration become difficult to eradicate, impeding the high-quality development of both data spaces and the broader data element market. Full Lifecycle Theory, with its emphasis on dynamic evolution and closed-loop governance of data processing, offers a systematic solution. Specifically, by integrating the dynamic evolution of trust along the temporal dimension with a structured, layered, and categorized interconnection of various Trusted Data Spaces in the spatial dimension, this paper proposes a new paradigm. This paradigm requires that a Trusted Data Space must enforce strict admission trust at the front-end, ensure process trust in the mid-stream, and realize outcome trust at the back-end. This improve the construction paradigm of a comprehensive full-process trust management system designed to enhance the sustainability and adaptability of data spaces, ultimately promoting the high-quality development of the digital economy.