Abstract: Topology identification of the network structure is fundamental for the optimization and control of distribution systems. With the high penetration of renewable energy generation, such as wind and solar power, the topology of distribution networks has become more complex and changes frequently, significantly increasing the difficulty of topology identification. To improve the accuracy of topology identification, this paper proposes a distribution network topology identification method based on a deep learning framework that combines Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN), taking into account the structure and operational characteristics of distribution networks. This method first uses Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensional voltage magnitude and active power data. It then employs SOM to extract data features and transform them into a two-dimensional feature map. Finally, CNN is used to learn the mapping between the input features and topology labels, enabling accurate identification of the distribution network topology. The effectiveness of the proposed method is validated through simulation experiments on 33-, 69-, and 123-bus distribution network cases. Compared to other methods, this approach demonstrates significant advantages in terms of identification accuracy and robustness.