［目的/意义］ 可信数据空间在支撑跨主体数据流通与智能服务供给的过程中，面临数据资源异构、算法模型不透明及主体协同信任不足等问题。针对这些问题，探讨可信数据空间中的数模协同转译机制与信任重构过程，为可信数据空间的数据安全流通与持续运行提供理论支撑。 ［方法/过程］ 引入行动者网络理论（ANT），依据广义对称性原则，将技术开发者、平台运营方、政策制定者等人类行动者，以及数据资源、算法模型、计算基础设施等非人类行动者纳入同一分析框架，识别可信数据空间数模协同过程中的关键异质行动者及其黑箱节点，并沿着问题呈现、利益赋予、征召与动员的转译链条，对数模协同转译机制及信任重构过程进行理论分析。 ［结果/结论］ 识别可信数据空间数模协同中的黑箱障碍，主要包括数据流通信任缺失、算法运行不透明以及数据与模型本体异质性等问题，并揭示3类障碍在协同过程中的相互作用关系；构建由问题呈现、利益赋予、征召与动员构成的转译链条；阐释可信数据空间的数模协同并非数据与模型的简单耦合，而是异质行动者通过持续转译形成可执行、可追溯的协同秩序，并推动信任重构的过程。

[Purpose/Significance] Trusted data spaces, in supporting cross-actor data circulation and intelligent service provision, are confronted with problems such as heterogeneous data resources, opaque algorithmic models, and insufficient collaborative trust among participating actors. To address these issues, this study explores the translation mechanism of data-model collaboration and the process of trust reconstruction in trusted data spaces, aiming to provide theoretical support for secure data circulation and sustainable operation of trusted data spaces. [Method/Process] Actor-Network Theory (ANT) was introduced as the analytical perspective. Following the principle of generalized symmetry, this study incorporated human actors, including technology developers, platform operators, and policy makers, as well as non-human actors, including data resources, algorithmic models, and computing infrastructures, into the same analytical framework. On this basis, the key heterogeneous actors and black-box nodes in the process of data-model collaboration in trusted data spaces were identified. Furthermore, along the translation chain of problematization, interessement, enrollment, and mobilization, the study theoretically analyzed the translation mechanism of data-model collaboration and the process of trust reconstruction. [Result/Conclusion] This study identifies the black-box obstacles in data-model collaboration in trusted data spaces, which mainly include the lack of trust in data circulation, the opacity of algorithmic operation, and the ontological heterogeneity between data and models, and reveals the interactions among these three types of obstacles in the collaborative process. It constructs a translation chain consisting of problematization, interessement, enrollment, and mobilization and explains that data-model collaboration in trusted data spaces is not a simple coupling of data and models. Rather, it is a process in which heterogeneous actors form an executable and traceable collaborative order through continuous translation, thereby promoting trust reconstruction. Specifically, data resources, algorithmic models, computing infrastructure, and human actors do not naturally achieve coordination merely through technical connection. Instead, they need to gradually define shared problems, align interests, embed roles, and stabilize collaborative networks through repeated interaction and negotiation. In this process, trust is not only supported by technical mechanisms but also generated and reinforced through the coordinated operation of standards, interfaces, rules, responsibilities, and governance arrangements.