Abstract: Magnetosphere substorm is the result of couplings between solar wind and magneto-sphere. In general, as AE, AL etc indices are used to inspect the turbulence in polar region when sub storms occur, they are the target indices in space environment prediction. The AE index is predicted by data of solar wind and interplanetary magnetic field （IMF）, with a back-propagation neural network, which is all-joint one. The data of input comes from ACE satellite, combined into 5 min resolution. There are 4 input variables： The By, Bz components of interplanetary magnetic field, the velocity of solar wind and the density of solar wind proton. The 3 networks with the length of input time series of 20, 40 and 60 minutes are constructed, trained and train them separately. Then the time series of variables on influencing the AE index is discussed. The predictions show that our network model can forecast the trend of fluctuation of AE index, having wonderful veracity in quantitatively computing index value. The correlation between four input variables （By, Bz, v, n） and AE index is very good. As the time extends, the forecasting precision needs some improvements.