Abstract: The rapid application of generative AI is profoundly reshaping the development of academic journals. Generative AI holds significant practical value for journal development: it improves editorial efficiency, optimizes topic planning and article selection, enhances communication and sharing, broadens readership influence, and facilitates the integration of academic journals with scholarly research while encouraging researchers to develop proficiency in AI tools. However, generative AI also brings substantial risks. It may weaken the subjectivity of journal editors, foster overreliance on AI that undermines independent judgment, and challenge the quality of academic research by contributing to homogenization and utilitarian tendencies. Furthermore, the mechanisms for its application and protection remain underdeveloped: relevant legal frameworks require improvement, and technical safeguards in journal publishing need strengthening. In response, it is essential to establish and refine operational mechanisms for generative AI in academic publishing. This includes clarifying its scope of application and quality requirements across stages such as topic planning, manuscript review, editing, typesetting, and proofreading; building and training editorial teams adept at using AI tools; advancing intelligent editing capacity; and improving institutional and technical support systems. These measures are vital to ensure the effective and sustainable integration of generative AI into the field of academic journals.