The invention discloses a deep-belief-network-based diagnosis method of a distribution network. The method comprises: original monitoring data of a distribution network are obtained; denoising and normalized modeling are carried out on the original data; hyper parameters of a distribution network fault diagnosis model are set; 70% of collection data are used as training samples to train the model;the rest of 30% of samples are used for testing the trained fault diagnosis model, wherein six outputted nodes are used for expressing probabilities of occurrences of two kinds of faults at three phases respectively and training is carried out again if the precision does not meet the requirement; the operation state of the distribution network is monitored by the model; and if a fault is caused,a fault type and a line are obtained. According to the invention, the deep learning theory is applied to the fault diagnosis of the distribution network; and changing characteristics of all collectiondata under various faults are learned automatically under the circumstances of complicated structure, many devices, and frequent data deficiency. Meanwhile, the good fault tolerance performance is realized; the fault diagnosis accuracy and timeliness of the distribution network are improved; and thus the distribution network can work stably and safely.