The traditional method of line selection for single-phase grounding fault in small current grounding system usually adopts the line selection model based on one-dimensional signal, which has some problems such as low line selection accuracy and weak noise resistance. Based on the above problems, this paper proposes a line selection method for single-phase grounding fault of distribution network small current grounding system based on optimized VMD and dual-channel CNN-MATT: frost and ice algorithm is used to optimize the decomposition layer number and penalty factor of VMD, and fuzzy entropy algorithm is used to select the IMFs component with minimum fuzzy entropy as the noise reduction output signal. Gram-angle field algorithm is used to transform the denoised signal into two-dimensional spatial domain image, and the fault database is constructed. The GASF and GADF images are taken as the input of dual-channel neural network, and the fault features contained in the images are extracted and learned by CNN-MATT, and the fault lines are selected. In order to verify the effectiveness of the proposed method, MATLAB/Simulink and RTLAB closed-loop simulation platform are used in this paper, and the proposed model is compared with three kinds of line selection models under the premise of adding noise. The experimental results show that the accuracy rate of the proposed algorithm is as high as 99.4%, and it can maintain an accuracy of more than 95% under different noise conditions, which is superior to the other three line selection models, and overcomes the problems of low accuracy and poor noise resistance of traditional fault line selection methods.