Single-phase grounding faults frequently occur in low and medium voltage (form 6 kV to 66 kV) distribution network of China. The fault characteristics caused by single-phase ground fault are weak, and the characteristics of different types are not very distinguished, which makes it difficult to identify their types. Thus this paper proposes a single-phase ground fault type identification method based on feature decomposition and deep learning. Firstly, this method performs preliminary processing on the fault record data collected by the distribution network using Hilbert-Huang Transform (HHT) to highlight the characteristics of different fault types; then designs a deep learning model ResNet18 to learn the complex non-linear characteristics of the fault event and identifies the specific fault type. The verification through the recorded wave data collected by a domestic true test site proves that the method proposed in this paper can accurately identify multiple types of single-phase grounding faults, which can provide a reliable basis for the subsequent formulation of targeted fault handling measures.