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Widely prevalent therapies encounter difficulties in addressing diverse anatomical defects and variable illness conditions due to their inflexible designs and limitations in empirical optimization. Efficient strategies are critical to restore mechanics, improve pathological microenvironments, enhance neovascularization, and adapt to anatomical defects and clinical conditions. Deep learning networks (DLN) excel at analyzing extensive nonlinear relationships, enabling predictions of biomaterial\u2011biological interactions, hence accelerating biomaterial development. This study presents a synergistic DLN and 4D printing approach to fabricate a microenvironment\u2010adaptive bioactive scaffold (MABS) for enhanced osteogenesis and angiogenesis. The scaffold integrates bioactive glass and a shape\u2010memory PgP matrix, with a multilayer perceptron (MLP) neural network optimizing its design via nonlinear parameter\u2010performance analysis. 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This paradigm shift\u2014employing DLN to integrate 4D printing dynamics, degradation kinetics, and multi\u2010scale biological responses\u2014transforms bone implants from static entities to dynamically adaptive systems, offering a scalable, intelligent framework for precise bone repair that rectifies the deficiencies of current strategies.<\/jats:p>","DOI":"10.1002\/advs.76351","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T13:48:23Z","timestamp":1782827303000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Network\u2010Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration"],"prefix":"10.1002","volume":"13","author":[{"given":"Xiongjie","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Orthopedics Second Affiliated Hospital of Harbin Medical University  Harbin 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