Aircraft structure damage seriously affects the aircraft flight safety. In order to effectively identify aircraft composite structure damage, a new method combining general regression neural network (GRNN) and extreme learning machine (ELM) of composite structure damage diagnosis is proposed in this paper. Firstly, the data of fiber optic sensor on composite material laminated plates are gathered and pre-processed after striking and stretching on composite laminated plates. Secondly, strain information is decomposed by variational mode decomposition (VMD), and intrinsic mode functions (IMFs) are obtained. Meanwhile, the singular entropy feature of each IMF is derived. Then, a feature vector is built by kernel independent component analysis (KICA). Finally, the fusion feature vector is used to build GRNN-ELM identification model. Experimental data verify the effectiveness of the GRNN-ELM method,and the result shows that the GRNN-ELM model can realize aircraft composite structure damage identification more effectively compared with ELM and GRNN models, respectively, thus it has a good engineering application value.