Since the catenary of high-speed railway will produce pantograph arc, it is harmful to pantograph system, in order to reduce pantograph damage. A current time series coding technology, namely, the Gram angle summation / differential field (GASF/ GADF) is proposed. Because the current signals of different current receiving states are different, the images formed by their time series coding are also different, which makes computer vision technology can be used for time series classification to identify pantograph arcs. A total of five groups of pantograph receiving experiments were carried out under different conditions to measure the current data in pantograph system under different conditions, and the current data obtained from pantograph experiments were divided into normal receiving state and arc receiving state. By constructing a neural network and extracting the arc current signal, it visually demonstrates the abstract feature extraction of the CNN from the arch-net arc data in the form of a Gram angle field (GAF) image. The experimental results show that the method in this paper can accurately identify pantograph and network arcs under different conditions, avoiding the problem of video image background changes, and provides an idea for pantograph and network arc fault identification.