The present invention discloses a method for classifying grid equipment based on the convolution neural network. The method comprises that Step 1, a convolution neural network model is trained according to the existing training set and test set of the grid equipment image, and the input layer preprocesses the input image data, so as to increase the amount of data, wherein the number of convolution layers is not greater than N, and N+1 is the number of convolution layers of the common convolution neural network; and Step 2, the trained convolution neural network model is used to carry out classification on grid equipment images that need to be classified. According to the method disclosed by the present invention, the data enhancement technology is used to preprocess the input image data to increase the amount of data, so that the problems that the network is over-fitted and the precision is decreased due to that the amount of data is insufficient are solved; and considering that the amount of the trained data is relatively small, the number of the convolution layers and the number of convolution kernels are reduced while the size of the convolution kernel is increased and the size of the feature map extracted by each layer of the convolution layers is reduced, so that the number of features extracted by the convolution layers is reduced, the effect of preventing the over-fitted phenomenon is reached, and accuracy is improved.