To address the limitations of traditional distribution network fault location methods, which rely on a single fault diagnosis model, a new fault location method for distribution networks based on image fusion and dual-channel convolutional neural networks is proposed. The aim of this study is to improve the accuracy of existing methods under complex conditions such as high-resistance grounding, noise interference, distributed power supply grounding, and unsynchronized sampling times. First, the zero-sequence current signals are converted into two-dimensional images using Gramian angular summation field (GASF) and Gramian angular difference field (GADF) techniques, providing a basis for image processing. Next, image fusion technology is employed to spatially fuse the GASF and GADF images, resulting in a comprehensive feature image that fully leverages the characteristics of different images, thereby enhancing the richness and effectiveness of feature representation. Subsequently, a dual-channel convolutional neural network model is constructed, where a one-dimensional convolutional neural network and a ResNet50 network are used to extract features from zerosequence current signals and Gramian angular field images, respectively. This design takes full advantage of the strengths of different convolutional neural networks in processing one-dimensional signals and two-dimensional images. Finally, the fused features are input into a Sigmoid function to achieve fault line selection. Experimental results show that this method outperforms traditional methods under various complex conditions, with an accuracy rate, Kappa coefficient, Matthews correlation coefficient, and recall rate of 99.97%, 0.999 3, 0.999 3, and 0.999 5, respectively. These results indicate that the proposed method not only has high accuracy but also exhibits good robustness and stability, effectively addressing challenges such as high-resistance grounding, noise interference, distributed power supply grounding, and unsynchronized sampling times in practical applications. The proposed method provides a novel and efficient solution for fault location in distribution networks, with significant practical application value and broad prospects for promotion.