数算融合网络是为数据空间应用定制网络服务的智能通信网络基础设施，对推动数据空间构建、数据要素流通、算力和数据融合具有促进作用，可为数据确权、流通和交易等新的经济增长点提供技术支撑。本文在介绍数算融合网络内涵的基础上，概述了其数据平面、控制平面、编排层具备的关键功能，梳理了我国发展数算融合网络的宏观发展需求，详细讨论了数算融合网络技术的发展现状和国际态势。进一步研判了数算融合网络端侧、数据中心内、数据中心出口、数据中心间、算力中心间、数据和算力中心间、控制层、编排层、安全体系等方面的关键技术，列举了数算融合网络的应用场景和具体案例，包括“东数西算”枢纽互联、城市算力网、工业外网互联、能源设施互联、行业大模型。在分析我国数算融合网络技术发展面临的挑战后，研究建议：构建支撑行业大模型高质量发展的公用专网；推动数算融合网络科学装置建设，服务国家科学发展；依托数算融合网络，推动数据空间成果落地；开展大规模算力协作，突破单点算力不足瓶颈，为数据空间网络基础设施发展提供参考。

The data and computing convergent network (DCCN) is an intelligent communication network infrastructure tailored for data space applications. It promotes data space construction, data elements circulation, and integration of computing power and data. It also provides technical support for economic growth related to data rights, data circulation, and data transactions. This study defines the DCCN, outlines the key functions of its data plane, control plane, and orchestration layer, reviews the macro development requirements of the DCCN, and discusses the current development status and international trends of DCCN technologies. It further studies key DCCN components and technologies, including end sides, internal networking of data centers, data center exits, communication among data centers, networking among computing centers, networking between data and computing centers, control layers, orchestration layers, and security systems. Moreover, the study introduces application scenarios and cases of DCCNs, including east‒west data-center interconnections, computing power networks in urban areas, industrial extranet interconnections, energy facility interconnections, and industry-scale large models. Research also covers the challenges of developing DCCNs, based on which, we propose the following suggestions for developing the DCCN: (1) establishing public specialized networks that support the high-quality development of industry-scale large models; (2) promoting the construction of DCCN-based scientific facilities, serving national scientific development; (3) using the DCCN to promote applications in data spaces; (4) carrying out large-scale collaboration among computing facilities to overcome the bottleneck of insufficient computing capability at a single point. The suggestions aim to provide references for the development of DCCN infrastructures.