Following the launch of the National Education Digitalization Strategic Action 2.0 in 2025, intelligent technologies in educational publishing have begun to move beyond isolated tools and into the full publishing and learning-service chain. This shift is accelerating the transformation of traditional publishing institutions into providers of smart education services. To clarify how this transformation is taking place, the study organizes representative technologies according to their functional logic and examines their roles in content production, learning support, and teaching services. This functional approach makes it possible to compare technologies by the educational tasks they support rather than by technical novelty or market visibility alone. It then identifies the obstacles that prevent promising applications from becoming stable, pedagogically sound, and sustainable services. Four groups of application scenarios are examined. First, large language models and artificial intelligence-generated content are reshaping content production through domain-specific models based on textbook corpora, private deployment, human–AI collaboration, and platform-based proofreading. Second, knowledge graphs and adaptive technologies reorganize textbook content into networks of knowledge nodes, support multidimensional learner profiles, and enable learning diagnostics and personalized assistance. Third, augmented and virtual reality and spatial computing create immersive contexts that can reduce the cost or risk of practical training and enrich cultural learning experiences. Fourth, intelligent agents and digital humans extend interactive services through multi-turn dialogue, teacher workload-reduction assistants, learning companions, and role-based digital characters. Despite this expansion, implementation faces five interconnected bottlenecks. The efficiency-first logic of technology does not always fit dynamic teaching processes; separately developed systems fragment data and interrupt the continuity of learning services; hallucinations and algorithmic bias threaten scientific accuracy and assessment fairness; the principle of data minimization can conflict with cloud-dependent deployment; and weak pricing autonomy for artificial intelligence services can intensify homogeneous competition and undermine viable business models. These problems show that technical availability alone does not guarantee educational value. Applications must be evaluated against pedagogical principles, content quality, data compliance, organizational coordination, and sustainable operation. The study therefore proposes five development pathways. Educational value should be the primary premise of scenario design, with technologies assessed against the developmental and pedagogical principles of basic education. Scenarios should be reconstructed around authentic teaching needs, complex tasks should be decomposed into usable micro-tools, and data should be connected across the service process. Publishing institutions should build a reliable data foundation from their content resources by advancing knowledge digitalization and trusted data spaces. They should also embed content review, data-security controls, and compliance checklists throughout business workflows. Finally, open collaboration should support ecosystem co-construction and experimentation with models such as industry–academia–research scenario laboratories and software-as-a-service subscriptions. The core competitiveness of educational publishers lies in converting copyrighted content, teaching-research expertise, and contextual knowledge into dependable intelligent services that integrate technology with pedagogy and publishing standards.