The magnetic compensation of aeromagnetic survey is an important calibration work, which has a great impact on the accuracy of measurement. In an aeromagnetic survey flight, measurement data consists of diurnal variation, aircraft maneuver interference field, and geomagnetic field. In this paper, appropriate physical features and the modular feedforward neural network ( MFNN) with Levenberg-Marquard ( LM) back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data. LM algorithm is a kind of least square estimation algorithm of nonlinear parameters. It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method. LM algorithm\' s computing efficiency is high and fitting error is very low. The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON ( T-L) model with the linear least square ( LS) solution by fitting experiments with five different aeromagnetic surveys\' data.