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.