This Magnetic exploration is a geophysical exploration method to study geological structure and mineral resources. Inversion is an effective method to estimate the horizontal location,depth,and geometry of subsurface geological bodies. To resolve the problems of traditional inversion methods, such as dependence on initial model and long calculation time, we proposed a 2D inversion method of magnetic anomaly data based on Deep Learning.With this method, a number of magnetic anomalous body models were designed to perform forward simulation, which generated sample dateset, firstly; a new convolution neural network(CNN) magnetic inversion network was designed, secondly; the sample dataset was used to train the network thirdly; and inversion experimental was performed to evaluate the proposed method lastly. The experimental results show that the proposed method can invert position and magnetization of magnetic anomaly, with strong learning ability and certain generalization ability, and can solve the magnetic inversion problem effectively.