Entropy weight method (EWM) is a widely used weighting approach in water quality evaluation that assigns weights according to the discriminating degree of indicators. A higher discrete degree corresponds to a larger weight requirement, and vice versa. By using two water quality evaluation examples, this study proves that the weighting result of EWM cannot accurately reflect the information content and discriminability of indices in many conditions. For the EWM that uses the directly generating quotient (DGQ) in standardization, when the concentration dataset contains many zero values, the EWM results become prone to distortion. Similarly, for the EWM that utilizes the generating quotient after range pretreatment (GQARP) in standardization, when similar maximum values are present in the concentration dataset, the EWM results become prone to distortion. From the distortion weighting results of EWM, those indicators with high pollution degrees can be easily neglected, thereby leading to overoptimistic comprehensive water quality evaluation results. Although the source of distortion in the EWM results can be traced to the standardization and ranging processes, a solution to this problem is not yet available. In sum, the conventional EWM cannot correctly reflect the distinction of water quality indices; and it cannot be directly applied in water quality evaluation.

熵权法（EWM）是水质评估中广泛使用的加权方法，它根据指标的区分程度分配权重。较高的离散度对应于较大的重量要求，反之亦然。通过使用两个水质评价实例，该研究证明了EWM的加权结果不能在许多条件下准确反映信息的内容和指标的可分辨性。对于在标准化中使用直接生成商（DGQ）的EWM，当浓度数据集包含许多零值时，EWM结果易于失真。同样，对于在标准化中利用距离预处理后的生成商（GQARP）的EWM，当浓度数据集中存在相似的最大值时，EWM结果变得容易失真。从EWM的失真加权结果来看，那些污染程度高的指标很容易被忽略，从而导致水质综合评价结果过于乐观。尽管EWM结果中的失真源可以追溯到标准化和测距过程，但尚无解决此问题的方法。总而言之，传统的EWM无法正确反映水质指标的差异。不能直接应用于水质评价。尽管EWM结果中的失真源可以追溯到标准化和测距过程，但尚无解决此问题的方法。总而言之，传统的EWM无法正确反映水质指标的差异。不能直接应用于水质评价。尽管EWM结果中的失真源可以追溯到标准化和测距过程，但尚无解决此问题的方法。总而言之，传统的EWM无法正确反映水质指标的差异。不能直接应用于水质评价。