In this paper, a quantitative method for fatigue crack diagnosis of composites is proposed, which combines guided wave and strain multi-source heterogeneous feature fusion with deep learning. Two types of sensors, i.e. piezoelectric sensor and fiber Bragg grating sensor, are used to measure the global and local response of composites under fatigue loading. The one-dimensional signal of the guide wave is encoded into an image by the Gram angular field transform method. The deep features of the image are further extracted by the AlexNet deep learning model. Furthermore, a heterogeneous feature fusion based on image square array of the guided wave and strain is presented. Finally, the CNN models with different input features are constructed. The results show that the quantitative diagnosis of the hole-edge crack based on the heterogeneous fusion of the guided wave and FBG is more accurate and reliable than that based on the single feature of guided wave.