As composite materials are playing a more and more important role in advanced aircraft structures, the change of mechanical properties of composites during service is very important to the overall safety of the aircraft. In order to achieve the goal of fatigue evaluation and life prediction of composite components of aircraft based on guided wave in-situ detection. This paper carries out research on three aspects: the degradation law of structural mechanical properties, the influence mechanism of fatigue accumulation on guided wave propagation, and methods on fatigue characterization and life prediction. First of all, the fatigue evolution law of composite materials is studied from the perspectives of macroscopic phenomenology and microscopic physics. Then, the potential of guided wave phase velocity and mode conversion phenomenon for fatigue characterization is discussed through analyzing the guided wave field. At the same time, a deep learning framework is constructed to extract fatigue evolution features from the guided wave field in a data-driven manner. Finally, a fatigue evolution model based on the Bayesian model averaging method is proposed to predict the residual fatigue life of the composite specimen. Results show that: by extracting and analyzing the guided wave propagating features, the fatigue state of composite materials can be accurately characterized. Combining the Bayesian model averaging method and the confidence interval criterion, the goal of residual life prediction before specimen fatigue failure can be achieved.