To analyze the functional requirements for artificial intelligence(AI) technology applied to academic journal peer review, a method of analysis for the functional requirements of AI-assisted academic journal peer review is proposed by introducing the attractive quality theory and the analysis tool of Kano model. The questionnaire survey method is used. Nine functions of AI-assisted academic journal peer review are divided into three categories via Better-Worse coefficient analysis, i.e., the essential attribute, the one-dimensional attribute, and the attractive attribute. Subsequently, four important functions to be developed or optimized with much emphasis are identified, and the corresponding suggestions are made. The research results provided the theoretical method and data support for the functional requirement analysis of AI-assisted academic journal peer review.