图书情报工作

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2026 , Vol. 70 >Issue 15: 60 - 76

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.15.005

研究论文

基于区块链技术的创新评审机制研究——以论文同行评审为例

  • 周志刚 ,
  • 曲景辰 ,
  • 罗翠线 ,
  • 窦路遥
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  • 1 山西财经大学信息学院 太原 030006;
    2 中国科学院武汉文献情报中心 武汉 430071;
    3 中国科学院大学经济与管理学院信息资源管理系 北京 100190
周志刚,副教授,博士,硕士生导师;曲景辰,硕士研究生,通信作者,E-mail:qujingchen0512@163.com;罗翠线,副教授,博士,硕士生导师;窦路遥,博士研究生。

收稿日期: 2025-06-03

  修回日期: 2025-08-12

  网络出版日期: 2026-07-30

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Research on an Innovative Blockchain-Based Review Mechanism: A Case Study of Academic Peer Review

  • Zhou Zhigang ,
  • Qu Jingchen ,
  • Luo Cuixian ,
  • Dou Luyao
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  • 1 School of Information, Shanxi University of Finance and Economics, Taiyuan 030006;
    2 National Science Library (Wuhan), Chinese Academy of Sciences, Wuhan 430071;
    3 Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
Zhou Zhigang, Associate Professor, PhD, Master's Supervisor; Qu Jingchen, Master's Candidate, Corresponding Author, E-mail:qujingchen0512@163.com; Luo Cuixian, Associate Professor, PhD, Master's Supervisor; Dou Luyao, Doctoral Candidate.

Received date: 2025-06-03

  Revised date: 2025-08-12

  Online published: 2026-07-30

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摘要

[目的/意义] 创新评审通过优化评审流程，有助于推动学术进步并促进科研过程的公平公正，但现有评审机制存在公平性低和激励不足的问题，因此亟需探索能够提升分配公正性并强化激励约束的新型评审模式。利用区块链技术，通过去中心化和智能合约实现评审过程透明、可信且自动化，旨在提升评审分配公正性并强化激励约束机制。[方法/过程] 以论文同行评审为例，提出一种基于区块链架构的创新评审模型——Mercury。该模型将审稿网络中不同实体及其交互行为设计为元路径，在此基础上进行算法与智能合约构建，并进一步引入区块链平台与审稿人之间的演化博弈模型，对关键影响因素开展仿真分析。[结果/结论] ①元路径精细刻画作者、论文、审稿人及区块链平台间交互，提升任务分配精度与公平性，使系统效用收敛至帕累托最优。②基于元路径的动态信誉评估实现激励与约束精准平衡，显著提高平台与审稿人双重效用。③完整模型在平台效用、审稿人效用及社会福利方面均优于各消融版本，验证元路径驱动机制的关键作用。④仿真结果表明，在差异化奖励-惩罚策略下，合规审稿效用稳定提升且显著高于恶意审稿，保障审稿人长期理性行为。⑤基线对比实验表明，Mercury框架在准确率与召回率上分别较最佳对照方法(Agent Review)提升了11%和12.5%，且将Gini值降至0.22，显著优化了推荐分布的相关性与公平性。

关键词： 区块链; 元路径; 同行评审; 博弈; 仿真模拟

本文引用格式

周志刚 , 曲景辰 , 罗翠线 , 窦路遥 . 基于区块链技术的创新评审机制研究——以论文同行评审为例[J]. 图书情报工作, 2026 , 70(15) : 60 -76 . DOI: 10.13266/j.issn.0252-3116.2026.15.005

Abstract

[Purpose/Significance] Innovation review helps to promote academic progress and promote the fairness and justice of the scientific research process by optimizing the review process. However, the existing evaluation mechanism has the problems of low fairness and insufficient incentives. Therefore, it is urgent to explore a new evaluation model that can improve the fairness of distribution and strengthen incentive constraints. Using blockchain technology, the review process is transparent, credible and automated through decentralization and smart contracts, aiming to improve the fairness of review allocation and strengthen the incentive and restraint mechanism. [Method/Process] Taking peer review as an example, this paper proposes an innovative review model based on blockchain architecture——Mercury. It designs different entities and their interaction behaviors in the review network as meta-paths. On this basis, the algorithm and intelligent contract are constructed, and the evolutionary game model between the blockchain platform and the reviewer is further introduced to simulate and analyze the key influencing factors. [Result/Conclusion] ① The meta-path finely characterizes the interaction between authors, papers, reviewers and blockchain platforms, improves the accuracy and fairness of task allocation, and makes the system utility converge to Pareto optimality. ② The dynamic reputation evaluation based on meta-path achieves a precise balance between incentives and constraints, and significantly improves the dual utility of the platform and reviewers. ③ The complete model is superior to each ablation version in terms of platform utility, reviewer utility and social welfare, and verifies the key role of the meta-path-driven mechanism. ④ The simulation results show that under the differentiated reward-punishment strategy, the utility of compliant reviewers is steadily improved and significantly higher than that of malicious reviewers, which ensures the long-term rational behavior of reviewers. ⑤ In baseline comparisons, Mercury achieves improvements of 11 % in Precision@10 and 12.5 % in Recall@10 over the best alternative (Agent Review) and reduces the Gini coefficient to 0.22, thereby markedly enhancing both the relevance and fairness of recommendation distribution.

Key words： blockchain; meta-path; peer review; game theory; simulation

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