In the age of intelligent media, generative artificial intelligence is driving a profound transformation in the global publishing industry. However, the practical results of the integration of traditional and emerging publishing have consistently fallen short of expectations. The core reason for this gap lies in the insufficient deep collaboration among industry, academia, and research institutions, which makes it difficult for theoretical research insights to effectively guide industrial practice, while real-world industry challenges remain inadequately addressed by targeted academic solutions. Currently, academic research in the field of publishing integration exhibit several limitations, such as a technological bias, thematic misalignment, and breadth over depth, thereby failing to effectively lead industrial development. At the industrial level, publishing integration faces four major challenges. First, the central role of publishing organizations is weakening: book bloggers and knowledge creators repackage books into fragmented formats such as short videos or comics, forming a trend characterized by "technology-led rather than content-led" dissemination. Second, the lack of unified data standards impedes progress: the high-quality content of traditional publishing faces digitalization obstacles, and practitioners' limited proficiency in advanced technological tools weakens the foundation for technology application. Third, copyright regulations remain chaotic: AI uses publishing resources as training data, and complex infringement scenarios blur the boundaries of ownership and responsibility, leaving publishing organizations vulnerable to infringement and thus dampening their enthusiasm for participation in integration. Fourth, a structural talent gap persists: traditional linear, departmentalized workflows and talent models are incompatible with the logic of intelligent publishing, leading to conflicts between content-focused teams emphasizing culture value and technology-oriented teams prioritizing efficiency. Therefore, strengthening industry-academia-research collaboration is imperative. First, the value foundation can be consolidated: academia should cultivate publishing talents with cultural resilience to reshape the central role of publishing organizations. Second, data standards should be unified: universities need to leverage interdisciplinary resources to develop dedicated metadata standards and knowledge graph tools for the publishing field, translating them into industry guidelines; simultaneously, they should offer appropriate courses to enhance the skills of incumbents, while also embedding data literacy training into undergraduate curricula. Third, copyright governance requires innovation: academic institutions should form interdisciplinary teams to provide theoretical support for copyright issues in the AI era; while think tanks should develop infringement detection technologies and compile industry white papers to foster industry consensus. Fourth, talent cultivation should be restructured: expand practical training in integration laboratories, implement interdisciplinary projects, establish innovative evaluation standards, and introduce incentive mechanisms to cultivate "publishing +" interdisciplinary talents. Looking ahead, high-quality publishing integration requires building a dual mechanism of "technology empowerment + value reconstruction". Deepening industry-academia-research collaboration will promote the transition of traditional publishing from "superficial integration" to "deep integration". Future efforts should prioritize the ethics of intelligent publishing, balance technological innovation with cultural heritage, explore differentiated pathways tailored to local conditions, and ultimately achieve a win-win situation for both the cultural and economic value of publishing.