Abstract: The classification of low-voltage distribution network is conducive to improving the efficiency of formulating economic operation management measures and new energy planning operation schemes of low-voltage distribution network. With the continuous access of various new sources of energy, charging piles and other new sources, the original load characteristics of the low-voltage distribution network have changed, which on the one hand leads to complex load characteristics of the distribution network, and on the other hand leads to less available load characteristic data after the change, which brings challenges to the classification of the distribution network. Aiming at the above challenges, this paper proposes an adaptive classification method of low-voltage distribution network based on CNN-AE-MAML. Firstly, convolutional neural network auto encoder（CNN-AE） is used to extract the dimensionality reduction features of the distribution load of low-voltage distribution network and the photovoltaic power generation. Spectral clustering（SC） was used to classify low-voltage distribution networks. Then, the distribution network type identification method based on softmax is constructed to identify the distribution network type by using the dimensional-reduction features of the actual data of low-voltage distribution network. In addition, the model agnostic meta-learning（MAML） method is used to train the CNN-AE feature extraction model, so that the CNN-AE model can adaptively extract the new load features of the distribution network under a small amount of data, and finally achieve accurate and fast adaptive classification of the low-voltage distribution network.