The invention discloses a cross-linked cable partial discharge mode recognition method based on a parameter optimization SVM (Support Vector Machine) algorithm. In order to avoid problems of a low neural network convergence rate, over learning and the like or low recognition precision due to improper SVM parameter section in traditional mode recognition, while an M-ary classification theory is introduced to extend the SVM algorithm with stronger generalization and learning abilities into multiple classifications of classifiers, an improved genetic algorithm is used for optimizing a penalty factor of each sub classifier and kernel function parameters, and thus the optimal parameter SVM classification model is built. Results show that each defect recognition rate is more than 95% when the optimal SVM serves as the classifier, and whether to optimize the parameters, the SVM general recognition ability is better than that of an RBF neural network.