摘要：
目的 构建覆盖全流程的数智化平台，提升《北京邮电大学学报》的出版质量与学术影响力，推动期刊数智化转型与生态重构。 方法 基于《北京邮电大学学报》实践，以“作者-读者-编辑-审稿-文章-科研-科普”7个层面为核心，构建7大系统，综合运用数据可视化、多模态融合、区块链存证、自然语言处理、人工神经网络、具身智能交互、多智能体协同、机器学习驱动、超长视频生成等先进技术，实现“创作-评审-加工-成果-受众-传播-评价”全链条的自动化与智能化管控。 结果 使用该平台，使审稿周期缩短30%，编校差错率降低25%，学术不端检出率提升至98%，创新性评估误差减少15%，显著提升了出版效率与内容质量，形成了“数据驱动-智能决策-动态优化”的协同生态。 结论 数智化平台不仅为《北京邮电大学学报》提供了全流程智能解决方案，更为我国科技期刊的转型升级提供了可复制的范式，在理论与实践层面为培育世界一流期刊提供了重要支撑。
关键词:
科技期刊,
人工智能,
人机协同,
智能审校,
数智化转型,
《北京邮电大学学报》
Abstract:
Purposes To establish a comprehensive digital intelligence platform covering the entire workflow， enhance the publication quality and academic influence of the Journal of Beijing University of Posts and Telecommunications， and drive the journal’s digital transformation and ecological restructuring. Methods Based on the practical experience of the Journal of Beijing University of Posts and Telecommunications， this study centers on seven core dimensions—"author-reader-editor-reviewer-article-research-popularization" as the core， we developed seven major systems. By integrating advanced technologies such as data visualization， multimodal fusion， blockchain-based evidence storage， natural language processing， artificial neural networks， embodied intelligent interaction， multi-agent collaboration， machine learning-driven algorithms， and ultra-long video generation， we achieved automated and intelligent control across the entire chain： "creation-review-processing-output-audience-dissemination-evaluation". Findings Utilizing this platform reduced review cycles by 30%， lowered editorial error rates by 25%， increased academic misconduct detection to 98%， and decreased innovation assessment errors by 15%. This significantly enhanced publishing efficiency and content quality， forming a collaborative ecosystem of "data-driven decision-making and dynamic optimization". Conclusions This digital-intelligence platform not only provides the Journal of Beijing University of Posts and Telecommunications with an end-to-end intelligent solution but also offers a replicable model for the transformation and upgrading of China’s scientific journals. It provides crucial theoretical and practical support for cultivating world-class journals.
Key words:
Scientific journals,
Artificial intelligence,
Human-machine collaboration,
Intelligent review,
Digitalization transformation,
Journal of Beijing University of Posts and Telecommunications
艾莉莎, 孙宇彤, 靳海灵, 马逍悦, 钟羽, 李博识, 李颖航, 黄超阳, 杨鹏达, 李致远. 科技期刊数智化平台建设创新方案及实测效果探析——以《北京邮电大学学报》为例[J]. 中国科技期刊研究, 2025, 36(10): 1327-1345.
AI Lisha, SUN Yutong, JIN Hailing, MA Xiaoyue, ZHONG Yu, LI Boshi, LI Yinghang, HUANG Chaoyang, YANG Pengda, LI Zhiyuan. Analysis of innovative solutions and empirical results in building digital intelligence platforms for scientific journals： Taking Journal of Beijing University of Posts and Telecommunications as an example[J]. Chinese Journal of Scientific and Technical Periodicals, 2025, 36(10): 1327-1345.