Aiming at the problem of decision error caused by similarity collision in evidence theory, a new combination rule for evidence theory was proposed. Firstly, the features of focal-element sequence in evidence were extracted and converted into a sort matrix to reduce similarity collision. Secondly, the weight of each evidence was determined based on sort matrix and information entropy. Finally, the Modified Average Evidence (MAE) was generated based on the evidence set and evidence weight, and the combination result was obtained by combing MAE for n-1 times by using Dempster combination rule. The experimental results on the online dataset Iris show that the F-Score of average-based combination rule, similarity-based combination rule, evidence distance-based combination rule, evidence-credit based combination rule and the proposed method are 0.84, 0.88, 0.88, 0.88 and 0.91. Experimental results show that the proposed method has higher accuracy of decision making and more reliable combination results, which can provide an efficient solution for decision-making based on evidence theory.