纤维增强复合材料（fiber reinforced composite, FRP）在制作使用过程中由于撞击损伤、疲劳积累等问题，导致内部结构发生不可逆的细微损伤，声发射技术（acoustic emission, AE）以其高精度、实时性，成为监测其损伤演化和失效机制的重要手段。综述了近年来声发射技术在FRP损伤表征中的应用，通过对参数分析、波形分析、模式分析及深度学习分析等AE技术手段展开研究，结果表明参数分析与波形分析可以针对检测过程中的信号特征进行互补，实现了对复合结构变形、断裂等损伤行为的定性描述，深度学习分析等方法为纤维增强复合材料的健康监测和寿命预测提供了重要理论支持。综合来看，声发射技术能够实时监控和评估运行中的复合材料结构，对于维护FRP材料健康状况、预防突发性故障具有巨大的发展潜力，未来可以进一步结合人工智能技术以提高损伤识别的准确性和效率。

During the manufacturing and application of fiber-reinforced composites (FRP), issues such as impact damage and fatigue accumulation cause irreversible subtle damage to the internal structure. Acoustic emission (AE) technology, with its high precision and real-time property, has become an important means to monitor the damage evolution and failure mechanisms of FRP. The applications of acoustic emission technology in the damage characterization of FRP in recent years was reviewed. By conducting research on AE technical means such as parameter analysis, waveform analysis, pattern analysis, and deep-learning analysis, the results showed that parameter analysis and waveform analysis could complement each other in terms of signal characteristics during the detection process, achieving a qualitative description of damage behaviors such as the deformation and fracture of composite structures. Methods such as deep-learning analysis provided important theoretical support for the health monitoring and life prediction of fiber-reinforced composites. Overall, acoustic emission technology can monitor and evaluate the composite structures in operation in real-time. It has great development potential for maintaining the health of FRP materials and preventing sudden failures. In the future, it can be further combined with artificial intelligence technology to improve the accuracy and efficiency of damage identification.