Abstract: Textile composites have been extensively utilized in sectors such as aerospace and automotive manufacturing due to their significant advantages, including lightweight properties and high specific strength. However, the inherent structural complexity and multi-scale characteristics of these materials pose substantial challenges to performance prediction, structural design, and defect detection. Deep Learning (DL) technology, characterized by robust nonlinear modeling and large-scale data processing capabilities, offers an innovative technical pathway for textile composite research. This paper systematically reviews the research progress of DL in the field of textile composites, focusing on application achievements across five core areas: morphological characterization, defect diagnosis, structural optimization design, functional performance prediction, and damage analysis. Furthermore, the paper analyzes critical challenges currently facing the field—such as data scarcity combined with the "curse of dimensionality," lack of model interpretability, and difficulties in modeling complex constitutive relationships—while proposing corresponding solutions. Finally, future development trends are discussed to provide a strategic reference for the intelligent R&D and engineering application of high-performance textile composites.