In the field of multiple attribute decision making, the classic weighing values for objective index using maximum
entropy model are mainly based on the difference of the value of evaluation indexes, which easily results in the wrong
judgment that some index is not important(small index weight) because of the small difference degree, so that the decision
deviation is produced. The grey correlation deep coefficient is proposed to characterize the information size contained by
objective index weight. And the maximum entropy optimization model of objective weight is established by combining
with the maximum entropy criterion to compute the objective weight of multiple attribute decision, which solves the classic
model’s serious defect better. Finally, the comparison analysis of the actual cases demonstrates the good performance of the
proposed method.