运用马氏距离替代欧式距离改进传统的TOPSIS方法,解决当属性间存在线性相关时欧式距离失效的缺陷;充分考虑对立集合并引入联系向量距离,解决可能存在的方案距离正理想解和负理想解距离都近的缺陷.然后通过决策者偏好系数将马氏距离和联系向量距离所得结果合成新的相对贴近度,从而同时克服传统TOPSIS方法的以上两个缺陷.最后通过供应商选择的实例来验证方法的有效性. The traditional TOPSIS method is improved by using the Mahalanobis distance instead of the Euclidean distance to solve the defect that the Euclidean distance fails when there is a linear correlation between the attributes. The solution of the positive and negative ideal solutions Then the shortcomings of the nearest TOPSIS method are overcome.And then, a new relative closeness is obtained by using the preference coefficient of the decision maker to combine the results of the Mahalanobis distance and the contact vector distance, so as to overcome the above two defects of the traditional TOPSIS method at the same time. The effectiveness of the method.