钛合金是航空、航天、海洋装备等关键领域的战略材料，其传统研发模式因依赖“试错”而效率低下。 人工智能(artificial intelligence, AI)的兴起正推动该领域向“数据与知识双驱动”的范式变革。 本综述系统阐述通过知识与数据融合，驱动先进钛合金实现从数据构建、性能预测到智能制造的全链条创新。 首先剖析了 AI 带来的范式变革与全球战略背景；其次，分别阐述了在构建钛合金领域知识体系中的应用、机器学习在钛合金成分智能设计中的实现、机器视觉在智能焊接与质量调控中的支撑作用，以及由 AI 智能体融合知识、数据与执行能力，实现自主研发；最后，对钛合金自主化研发的未来路径进行展望。 尽管面临数据、模型可解释性及产业集成等挑战，未来通过推动数据-知识融合指导、自主实验室转化，AI 有望引领钛合金研发进入自主化新阶段，为国家高端材料自主可控提供核心动能。

Titanium alloys are strategic materials in key fields such as aerospace and marine equipment. However, theirtraditional research and development (R&D) model, which relies heavily on trial and error, is inefficient. The rise ofartificial intelligence (AI) is driving a paradigm shift in this field toward a "data and knowledge dual-driven" approach. Thisreview systematically elaborates on how the integration of knowledge and data drives full-chain innovation in advancedtitanium alloys, covering data construction, property prediction, and intelligent manufacturing. First, it analyses theparadigm shift brought by AI and the global strategic context. Second, the application of AI in constructing a titanium alloydomain knowledge system, the realization of machine learning in the intelligent design of titanium alloy compositions, thesupporting role of machine vision in intelligent welding and quality control, and the achievement of autonomous R&D byAI agents integrating knowledge, data, and execution capabilities are discussed. Finally, the future path of autonomousR&D for titanium alloys is anticipated. Despite challenges such as data quality, model interpretability, and industrialintegration, AI is expected to lead titanium alloy R&D into a new stage of autonomy through promoting data-knowledge fusion guidance and autonomous laboratory transformation, providing core momentum for the independent and controllabledevelopment of national high-end materials.