As traditional fault identification methods typically exhibit considerable processing complexity, are often time-consuming, and display a low degree of precision, a novel approach based on wavelet packet analysis using a support vector machine (SVM) is proposed in this study for the automatic identification of fiber defects in optical time domain reflectometry (OTDR). OTDR is initially used to acquire the original data of the fiber under test (FUT). Further, the event signs are decomposed by the optimal basic wavelet packet after the events are located, and the normalized energy features of the event signs as eigenvectors are extracted as input of training and testing based on the results of signal reconstruction. Finally, the SVM model is built,and fiber defects can be identified with the eigenvector as input. Subsequently, the SVM identification technique is used to obtain effective classification of the events as either reflection events, which are caused by connectors, or as non-reflection events, which are caused by bent events. In this study, two classification tests have been performed on a total of 2500 reflection and non-reflection events in airborne optical cable samples. The experimental results indicate that our method achieves a recognition rate of 99% in 3.03 s when the number of training samples is 1750 and when the number of testing samples is 750. Additionally, the recognition rate is increased by 2% and the recognition time is observed to be only 1% when compared to the previously proposed recognition method that is based on the backpropagation neural network. At present, the proposed method is successfully applied in the field detection equipment of airborne optical cable components independently developed.