It is difficult to determine the fault points in multi branch and hybrid short lines of a wind farm. Thus an intelligent single-phase grounding fault location method based on a Long Short Term Memory (LSTM) neural network is proposed. First, the electrical information of the measuring device at the head of collection line is read. An all-phase Fast Fourier Transform (apFFT) phase difference correction method is used to build the fault feature set when a single-phase grounding fault occurs in a wind farm. Then, the fault data of wind farm collecting lines are normalized, and the deep learning LSTM neural network is trained to establish the prediction model of a single terminal fault location. Finally, precise fault location is carried out through the LSTM neural network fault locator. PSCAD/EMTDC experimental results show that the prediction accuracy of the proposed method is higher than that of a back propagation neural network or an extreme learning machine algorithm. It is feasible to use it for different fault instances and transition resistances, and suitable for fault location of wind farm collecting lines. This work is supported by the National Natural Science Foundation of China (No. 51677072).