Series arc fault is an important cause of electrical fire, and effective detection can ensure the normal operation of lines and reliable work of electrical equipment. According to the difficulty of low voltage series arc fault detection, a recognition model based on asymmetric convolutional neural network is proposed to extract series arc fault information adaptively. To solve the problems of series arc faults with many types and hidden information, firstly, the time-domain data processing method of Gramian difference angular field is used to map the time-domain signals simulated by load into two-dimensional matrix after polar coordinate transformation and trigonometric transformation, so as to increase the space occupancy of fault data points and data association information. Then, in order not to increase the time cost and improve the recognition efficiency of the model, the residual neural network is improved by adaptive asymmetric convolution and multi-channel discrete attention mechanism as the series arc fault model in low-voltage lines. Finally, a container is used to encapsulate the trained fault identification model to realize the fast analysis of fault information. Verification shows that the recognition rate of series arc fault can reach 99. 95%, and it has good recognition effect.