Driven by open-source initiatives and cost-reduction trends in foundational large language models like DeepSeek, China's publishing industry is undergoing a comprehensive transformation, moving beyond point-based efficiency improvements toward a full-chain ecological restructuring. This research employs a mixed-methods approach, combining longitudinal case studies of leading publishing groups with in-depth interviews involving over 30 industry executives, technology developers, and policy analysts conducted between 2023—2025. Supportive policies and technological accessibility have accelerated the development of vertical-specific large models, with the number of registered generative AI services in publishing increasing by 78% in 2024 alone. Publishing institutions are actively reconstructing scenario-based toolchains, integrating multi-modal products, and engaging in cross-border collaborations to explore a new "human-AI collaboration" paradigm. However, the industry continues to face several core challenges: ambiguous strategic positioning among 68% of surveyed publishers, dual bottlenecks in data quality and technical capabilities, limited innovation in business models, and critical shortages in talent and funding. Looking forward, key trends include the reinforced centrality of professional models as strategic assets, the evolution of agents toward greater autonomy, and essential breakthroughs in copyright governance and AI talent development. The application of AI has systematically enhanced publishing efficiency across multiple dimensions. In editorial processes, AI-assisted editing and intelligent proofreading tools have reduced manuscript processing times by 30%—50%, while automated content generation systems have decreased production costs by approximately 40%. In marketing and distribution, data-driven reader profiling enables precise targeting with 25% higher conversion rates, and AI-powered recommendation systems have increased cross-selling opportunities by 35%. Furthermore, generative AI facilitates innovative content creation, Despite these advancements, significant challenges persist in four key areas. Copyright issues remain complex and multifaceted, with only 30% of publishers having established clear guidelines for AI-generated content ownership. The industry also faces substantial risks related to data quality and model reliability, as 65% of organizations report difficulties in obtaining sufficient high-quality training data. Technical implementation barriers affect 45% of medium-sized publishers, while return on investment (ROI) uncertainties cause 60% of traditional publishers to maintain cautious investment approaches. Moreover, the lack of high-quality, structured proprietary data limits the effectiveness of specialized models for 70% of publishers, creating a significant competitive gap between industry leaders and followers. Future development necessitates deeper vertical integration of AI into core publishing workflows, constructing data-driven ecosystems centered on user needs, and fostering industry-academia-research collaboration. Strategic priorities include developing adaptive AI governance frameworks, establishing industry-wide data standards, and creating continuous learning systems for workforce development. Implementation should focus on building modular AI architectures that enable gradual adoption, beginning with high-impact areas like content enrichment and personalized learning pathways. Ultimately, the industry must transition from traditional content production to building intelligent service ecosystems, with successful early adopters already deriving 40%—50% of their value from AI-enabled services, thereby reshaping its core value within the global AIGC landscape.