As the important part of traction power supply system, pantograph and catenary are related to the safety and stability of highspeed train. It is of great significance to identify pantograph arc as soon as possible. By calculating the " Z" friction rate which is more in line with the actual train operation, the running speed, contact pressure and contact current during train operation are adjusted in single variable to simulate the pantograph arc experiment under four different working conditions. Based on the experimental data, the features of pantograph catenary current are compared and analyzed by D-score at first, and the arc identification features and their significant identification intervals are selected. At the same time, a method for finding the suitable number of samples containing sufficient feature information is designed. Finally, seagull optimization algorithm is used to optimize support vector machine to model and identify pantograph arc. The test results and comparative analysis show that SOA-SVM can quickly and effectively model and identify pantograph catenary arc with an average recognition level of 98. 5% and an overall recognition level of more than 97%.