Aiming at the problems of complex data processing and difficult identification caused by noise interference and other factors in the process of fault type identification of distribution network, this paper proposes a fault type identification method of distribution network based on sparse representation. The collection of fault data does not rely on the Shannon Theory. The collected time domain three-phase voltage(TPV) and zero-sequence voltage(ZSV) are divided into training sets and test sets. Learn the characteristic information of the fault voltage of the training set by the K-SVD dictionary learning algorithm, and construct an over-complete dictionary that accurately matches the essential characteristics of various types of faults. Based on learning dictionaries, the orthogonal matching pursuit (OMP) algorithm is used to perform adaptive dictionary sparse decomposition of the original time-domain fault data of the test set, and the fault signal is decomposed into the product of the overcomplete dictionary and the sparse vector. Combined with the classification method based on sparse Representation(SRC), the classification of the reconstructed fault signal is achieved. The proposed fault classification method does not need to construct fault signal features manually and avoids the tedious process of fault signal feature screening. It has strong adaptability and is not susceptible to transition resistance and noise. MATLAB/Simulink simulation verifies the effectiveness and accuracy of the proposed method.