Epilepsy is caused by abnormal synchronous discharge of neurons in the brain, which constructs the main basis of its diagnosis. The use of complexity theory to study the epileptic signal has become a hot spot. The symbolic transfer entropy as a reflection of the degree of chaos of nonlinear system of indicators can be used as a characteristic of epilepsy. It plays an increasingly important role in the study of epilepsy in electro encephalogram signals (EEG) feature extraction. But symbolic transfer entropy is generally used to measure the dynamic characteristics and directional information between two variables and ignores the interaction between multivariate. Epileptic EEG signals are analyzed based on multivariate symbol transfer entropy. By choosing the lead signal and the signal length to analyze the robustness, the method can be used to distinguish normal person and patients with epilepsy. It is proved that the algorithm is robust and reliable for clinical diagnosis.