[目的/意义] 利用AI生成学术期刊论文内容的特征，构建多维度的特征分析框架，对于辨识稿件中不当使用AIGC的行为，解决期刊审稿复杂化的难题以及维护学术出版的质量与公信力具有重要意义。 [方法/过程] 从引文、数据、观点和结论、结构框架、逻辑关系、内容一致性、概念阐释和语言表达8个维度切入，深入分析不同AIGC值的期刊论文，揭示AI生成期刊论文内容的显性与隐性特征，结合AIGC-CHECK系统检测结果与AI生成稿件特征规律，双重验证其在审稿中的实用效果。在此基础上，构建快速筛查、深度验证和综合应对3个层面的应对策略。 [结果/结论] 相较于仅单纯依靠系统检测或仅凭借审稿经验，“双重验证法”能够更有效应对不当使用AIGC的行为，弥补单一审核手段的局限，为辅助提高审稿效率和出版质量提供实践价值。

[Purpose/Significance] Investigating the characteristics of AI-generated academic journal content and establishing a multidimensional analytical framework are crucial for identifying improper use of AIGC in manuscripts, addressing challenges in peer review complexity, and upholding the quality and credibility of academic publishing. [Method/Process] This study analyzed journal articles with varying AIGC values across eight dimensions: citations, data, viewpoints and conclusions, structural framework, logical coherence, content consistency, conceptual clarity, and linguistic expression. Both explicit and implicit features of AI-generated content were systematically extracted. The practical efficacy of this approach in peer review was validated through cross-verification between AIGC-CHECK system outputs and empirically observed characteristics of AI-generated texts. A tri-level strategy was subsequently formulated, encompassing rapid preliminary screening, in-depth verification, and comprehensive countermeasures. [Result/Conclusion] Compared to relying solely on detection systems or reviewers' experience, this approach offers a more effective method to address inappropriate AIGC usage, overcomes the limitations of single review techniques, and provides practical value in enhancing review efficiency and publication quality.