The accurate identification of over-voltages is the primary task when disposing over-voltage accidents. Because of the difficulty in identifying high-dimensional features by shallow classifiers

we proposed an internal over-voltage identification method based on atomic decomposition（AD） and convolution neural network（CNN）. The AD algorithm was used to decompose the three-phase voltages of the bus

and the optimal atoms were reconstructed according to the frequency to obtain the characteristic atomic spectrum in this method. Then characteristic atomic spectrum was input into the CNN to realize the identification of seven typical internal over-voltages. The method introduced in this paper was verified through the simulation and physics experiment platform. The results show that

compared with the shallow learning support vector machine and extreme learning machine

CNN has stronger self-learning ability; and the proposed method has a higher recognition rate and stronger adaptability than the traditional low-dimensional features combined with shallow learning algorithms. The identification method can be well applied to identify the internal over-voltage in the distribution network. The research can provide a reference for the identification of over-voltage in distribution network.