In view of such problems of traditionally BP neutral network-based magnetic hysteresis model as slow convergence speed and complicated artificial extraction of characterization parameters of magnetic characteristic in the modeling process

a kind of method to simulate the vector hysteresis characteristic on the basis of deep convolutional neural networks（DCNN）is proposed.The model takes the time series data of magnetic flux density and amplitude and phase of the magnetic field strength as the training data of the convolutional neural networks（CNN）.The residual error module is used to improve the convergence speed of convolution neural network.The improvement of the model can not only avoid the complicated artificial feature extraction process in the traditional magnetic hysteresis model

but also extend and apply for the setup of hysteresis models under non-sinusoidal excitation whose characteristic parameters are not easy to extract.It is founded through the comparison of magnetic hysteresis model of different network structures that the improved DCNN magnetic hysteresis model can not only reduce the number of iterations

but also assure refinement of the magnetic hysteresis behavior simulation.