[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.