A intrusion signal extraction and recognition method based on complementary ensemble empirical mode decomposition(CEEMD),singular value entropy and multiple kernel support vector machine(SVM) is proposed.Firstly,the intrusion signals were decomposed using the CEEMD and a series of intrinsic mode functions(IMF) were gotten.Subsequently,IMFs were decomposed by singular value decomposition(SVD) and singular value entropy was calculated.Then,according to the singular value entropy,the useful IMF component was selected,and the feature vector was constructed.Finally,the multiple kernel support vector machine was used to identify the intrusion signal.The experiments were carried out by using the actual intrusion signals,such as climbing,knocking,car,wind,and so on.The experimental results show that the CEEMD method can solve residual white noise of EEMD,and the multiple kernel SVM has better recognition rate than the single kernel SVM,and the climbing intrusion signal recognition rate is 95%.