Most current distribution network fault diagnosis schemes need to be supported by a large number of fault simulations. With the continuous expansion of the scale of the distribution network， the fault probability increases year by year. This kind of method can easily be limited by different fault types and numbers， resulting in a sharp increase in the amount of simulation calculation and difficulty in diagnosing quickly. Therefore， this paper proposed a fault diagnosis method based on unified features. Firstly， it used the voltage increment relationship of sparse measuring points to deduce the unified fault characteristics of the distribution network and introduced a neural network to build the fault diagnosis model. Combined with an example， the unified feature diagnosis method is tested， and its computational advantage is analyzed. After that， it extended the unified feature diagnosis method to a large-scale distribution network and realized the parallel diagnosis of each sub-network through the partition method. The results of several diagnosis examples show that the proposed method can diagnose effectively by using the sparse voltage increment value. The simulation times are independent of the fault type and number but only depend on the number of branches， which greatly reduces the amount of calculation， and has no strict synchronization requirements for the measured data.