Abstract: When the input includes several regimentations and the number of some variables in some regimentations is much more than that of the other regimentations,the former will weak the latter's effect on the output,which leads to the augment about the forecasting error of the model.The entropy based the self-accommodation back propagation neural model is introduced to solve this problem,in which the several variables of each regimentation are weighted according to their importance,so each regimentation is turned into one input respectively in the back-propagation (BP) net work model.The improved model can take the all kinds of inputs into account entirely and reasonably,and boost the forecast accuracy,which develops the applied theory of the neural network.