Abstract: To solve the invalidation problem of Dempster-Shafer evidence theory for high conflict，this paper improves D-S algorithm based on weighted average method.A new similarity function is proposed to represent similarity，and by normalized similarity，pretreated evidences are obtained support.Then，preprocessed evidences are fused by Dempster’s rule.The more expected result data shows that，compared with other methods，this new algorithm can reduce harmful influence of false evidence effectively，at the same time，has higher convergence rate and reduces decision risk.Finally，the new similarity function is analyzed to prove the rationality of improved algorithm.

Key words: fusion algorithm, D-S evidence theory, similarity function, weighted average

摘要： 针对D-S证据理论对于高冲突证据融合失效的问题，基于证据加权平均处理的方法，对D-S算法进行了改进。提出了一种新的相似度函数来表示证据的相似度，并通过归一化处理得到证据的支持度，对证据进行预处理，再采用Dempster规则融合证据。通过数值验证并与其他的改进算法对比，结果更为理想，它能有效地减小伪证据的不良影响，同时具有较高的收敛速度，降低了决策风险。对新的相似度函数进行分析，证明了改进算法的合理性。

关键词: 融合算法, D-S证据理论, 相似度函数, 加权平均