Generative artificial intelligence (AI) is profoundly reshaping education publishing. This paper, amid deep AI-publishing integration, adopts a multi-dimensional approach (theoretical analysis, case studies, and industry reviews) to examine its impacts, analyzing challenges, opportunities, and transformation pathways through leading publishers’ experiences. The methodology of this paper comprises a theoretical deconstruction of the interaction between generative AI and education publishing, establishing an analytical framework encompassing content production, knowledge dissemination, teaching models, and textbook functions. In-depth case studies of representative entities are conducted, including Higher Education Press, People’s Communications Publishing House, Springer Nature, and Microsoft’s phi-1 model development project, to examine specific impacts and adaptive strategies, while incorporating industry trend analysis based on policy documents, market data, and technological whitepapers to contextualize transformation dynamics. The challenges presented by generative AI manifest in four primary disruptions: transforming content production processes through intelligent writing tools that reshape the entire chain from topic selection and content creation to editing and proofreading; restructuring knowledge dissemination channels from one-way paper-based transmission to an open networked structure driven by intelligent algorithms enabling personalized content delivery based on user portraits; deconstructing traditional teaching models as the popularization of higher education increases demand for personalized learning, challenging "one-size-fits-all" static textbooks; and impacting the fundamental status of textbooks, with declining reliance on paper materials due to their slow update cycles compared to rapid iteration of cutting-edge knowledge. Amid these challenges, three key opportunities emerge: the digital reconstruction of knowledge value, where high-quality educational content accumulated by publishing institutions becomes scarce data assets for training professional AI models; the intelligent enhancement of educational services, as AI transcends traditional textbook limitations to enable knowledge graph-based personalized learning; and the platform-based reconstruction of industry ecology, with educational publishing shifting from a single content provider to a smart learning service provider covering the entire process of "teaching, learning, assessment, management, and research." To address these dynamics, this paper proposes a transformation path from content provider to smart learning service provider involving four dimensions: positioning calibration (adhering to educational values with AI-human dual review mechanisms), scenario innovation (building a "teacher-student-machine" tripartite collaborative learning paradigm), content upgrading (promoting knowledge datafication and assetization), and capability iteration (cultivating interdisciplinary digital teams). Taking Higher Education Press as a case study, this paper illustrates practices in strategic alignment (linking transformation to national strategies), organizational adjustment (breaking departmental barriers), technological empowerment (developing the LOVONG large model and multi-modal corpus), and product innovation (extending digital textbooks to full-process educational services, such as the "Artificial Intelligence Teaching Public Service Open Zone" on the National Smart Education Platform 2.0). Finally, this paper proposes policy support and data governance measures, including formulating policies for AI-publishing integration, establishing certification systems for high-quality datasets, and promoting collaboration among publishers, AI enterprises, and educational institutions. These findings, derived from integrated research methods, provide both theoretical and practical guidance for developing a new ecosystem of education publishing in the AI era.