摘要：

目的 针对科技期刊数字化转型中存在的系统割裂、数据孤岛及决策依赖经验等问题，构建一个覆盖出版全链条、可解释且可复制的数据决策支持平台，推动期刊运营实现“看得见、算得清、用得上、可闭环”的科学化管理。方法 以中华医学会杂志社数据中台的BI系统为基座，研发MedPress“数智中心”模块。通过构建“采集-存算-治理-资产-服务”一体化数据治理体系，整合多源异构数据，开发覆盖“采、编、产、传、评”全流程的可视化分析功能，为编辑与管理人员提供实时、多维的数据支持。结果 平台利用5.1亿条数据，上线6大功能模块，实现全流程关键指标动态监测。用户调研显示整体满意率超84%。平台已在作者分析、稿源评估、传播跟踪等场景中得到实际应用，有效支持期刊精细化运营与数据驱动决策。结论 “数智中心”初步实现了科技期刊全链条的“一站式、实时化、可视化、智能化”赋能，验证了平台化、模块化推进集群数字化转型的可行性，为行业提供了可复制、可扩展的系统范例。未来通过对接知识中台、深化学者画像、衍生主动传播和学术评价，可进一步推动期刊向数据驱动的知识服务生态演进。

关键词: 大数据, 科技期刊, 决策支持系统, 集群化出版

Abstract:

Purposes Aiming to address key challenges in the digital transformation of STM journals， such as fragmented systems， data silos， and experience-driven decision-making， this study seeks to develop an interpretable and replicable data-driven decision support platform that covers the entire publishing chain. The goal is to promote scientific management of journal operations， characterized by “visibility， measurability， applicability， and closed-loop improvement”. Methods Based on the data middle platform and business intelligence （BI） system of the Chinese Medical Association Publishing House’s MedPress platform， the “Digital & Intelligence Center” module was developed. By establishing an integrated data governance framework encompassing “collection， storage & computation， governance， assets， and services”， multi-source heterogeneous data were consolidated. Visual analytic functions covering the complete workflow，from submission， peer-reviewing， editing， production， dissemination， to evaluation，were designed to provide editors and publisher managers with real-time， multi-dimensional data support. Findings The platform leverages over 510 million data entries and has launched six functional modules， enabling dynamic monitoring of key indicators throughout the publishing process. User surveys indicate an overall satisfaction rate exceeding 84%. The platform has been practically applied in scenarios such as author analysis， manuscript source evaluation， and dissemination tracking， effectively supporting refined journal operations and data-driven decision-making. Conclusions The “Digital & Intelligence Center” has preliminarily achieved “one-stop， real-time， visual， and intelligent” empowerment across the entire chain of STM journal operations. It demonstrates the feasibility of advancing cluster-wide digital transformation through a platform-based and modular approach， offering a replicable and scalable model for the industry. Future integration with the knowledge middle platform， along with deepened scholar profiling， enhanced active dissemination， and evolved academic evaluation， will further drive the transition of journals toward a data-driven knowledge service ecosystem.

Key words: Big data, STM journals, Decision support system, Cluster publishing