The short-circuit currents distributed from distribution networks with small current grounding mode are relatively little after single-phase grounding faults. However, due to the aforementioned faults the long-term presence of arcing phenomena can increase the potential fire risk. In order to minimize fire threat, the existing methods for identifying fault types are based on whether the arcs occur or not. Whereas, the impact of arcs’ occurrence frequency is not taken into account. Aimed at the problem, on the basis of the actual cases of single-phase grounding faults in a certain distribution network the correlation between zero-sequence current characteristics and corresponding arcing phenomenon is firstly analyzed. And a novel classification method for single-phase grounding faults is proposed involving the arcs’ occurrence frequency. The waveforms characteristics of zero-sequence current are further extracted under the distinct fault types, such as “flat shoulder distortion”, “transient change” and so on. The aforementioned characteristics are mathematically described using the energy proportion of zero-sequence current components with different frequency bands, harmonic centroid and the arcing cycle number. Used these mathematical features as inputs, a fault-type identification model based on long short-term memory (LSTM) networks was developed. At last, the proposed model is tested with a dataset of 223 typical fault cases collected from a certain power company. It is verified that the accuracy rate of proposed model is 96.4%. The distinct fault types can be identified effectively. It is significant for reducing the fire risk and saving on the costs associated with the maintenance of distribution networks.