At present, there are few studies on fault location in a distribution network with limited measurement conditions. In addition, the traditional centralized fault location system of a master station has shortcomings in real-time and security. Thus, a single-phase ground fault section location method based on edge computing and deep learning is proposed. First, a multi-objective optimization model of edge computing unit configuration based on partition correction is constructed. The model reduces the communication delay of a fault location system and improves the security of data transmission by the partition correction method, thus ensuring the safe operation of the network. Second, a data-driven intelligent algorithm is applied to the fault section location. The variation of the steady-state effective value of the phase current before and after the fault is selected as the fault feature. A fully connected deep neural network is used to learn the mapping relationship between sample features and labels, and an offline trained location model is obtained and stored at the edge nodes to achieve fast fault location. Finally, the IEEE 33-bus system is taken as an example for simulation. The example shows that the model performs well with distributed generation access, high resistance fault, noise interference and topology change.