科技与出版Issue(1)：70-84,15.
可解构的"畅销经验":AIGC赋能出版营销的价值趋向与实践落点
Deconstructable Best-Selling Experience:Value Trend and Practical Applications of AIGC-Empowered Publishing Marketing
摘要
Abstract
With the rapid penetration of generative artificial intelligence(AI)into the publishing industry,publishing marketing,as a key link between production and consumption,is facing a key shift from experience-driven to mechanism-driven.Starting from the highly practical publishing proposition of best-selling experience,this study adopted a questionnaire survey(questionnaires were distributed in the publishing industry community and related WeChat official account online from late July to August 6,2025,and finally,1049 valid questionnaires were collected),in-depth interviews with experts(respondents covered state-owned publishing groups,professional publishing houses,private publishing enterprises and technical services and platforms,respondents have worked in their respective fields for a long time,and first-line practical and management experience),case comparison and literature analyses,in-depth analyses of the application status of AI-generated content(AIGC)technology in China's publishing industry,and made clear the specific links and application scenarios of AIGC-enabled publishing marketing.It is found that AI technology not only releases productivity at the efficiency end but also reshapes value generation at the levels of narrative organization,audience matching,immersion translation and organizational collaboration.In this study,best-selling experience is divided into three levels of knowledge practice:one is individual experience with editing as the core,which covers subjective cognitive resources such as topic selection judgment,text value judgment and border control;the second is the organizational experience focusing on the organization and brand level,which is reflected in the reusable template accumulated by the publishing category,brand style and audience stickiness;and the third is the industry experience embedded in the industry system logic,which emphasizes that published products have dual values of cultural attributes and commodity attributes and their public expectations should differ from those of ordinary consumer products,but they cannot be separated from the platform economy model and internet development logic constructed by data algorithms.This study emphasizes that the triple paths of individual experience,brand experience and industry experience are linked with each other,forming a continuous pedigree from individual skills and organizational ability to institutional environment,which gradually transforms occasional explosion experience into replicable long-term ability.In technological change,best selling is no longer the natural occurrence of results but rather a generation process composed of strategic design,technical ability and institutional arrangement.Experience is no longer an unspeakable intuitive judgment but a professional system that can be verified,transformed and passed on.Finally,copying experience involves copying the working methods and verification devices facing uncertainty so that experience can continuously generate new explanatory power and action forces on the premise of respecting category differences,channel heterogeneity and timing constraints.This study not only responds to the transformation needs of the publishing industry against the background of AIGC but also aims to clarify the redefinition of content logic,value negotiation and system construction after AI technology is involved in publishing marketing and provides theoretical support and practical reference for building a new data-driven publishing paradigm.关键词
出版营销/人工智能/畅销经验/出版融合/畅销书Key words
publishing marketing/artificial intelligence/best selling experience/publishing integration/best seller引用本文复制引用
宋俊锋,刘万..可解构的"畅销经验":AIGC赋能出版营销的价值趋向与实践落点[J].科技与出版,2026,(1):70-84,15.基金项目
中央高校基本科研业务费项目华东师范大学文理交叉跨学科培育项目"人工智能技术赋能新时代出版业高质量发展路径研究"(2025ECNU-WLJC010) （2025ECNU-WLJC010）
2025年优秀博士学术创新能力提升计划(YBNLTS 2025-028)的阶段性研究成果. （YBNLTS 2025-028）
