为更好地处理水质评价过程中的不确定信息，引入集对分析理论。针对水质分类级别细化问题，对集对分析理论中同、异、反联系度概念进行原创性扩展，将差异度细分为优异与劣异，对立度细分为优反与劣反。采用组合赋权进行指标权重计算，并考虑随机观测误差的影响对权重加以修正，同时利用差异系数与相关性优化指标体系。将模型应用于宜兴市地表水功能区水质评价，并将结果与人工神经网络、灰色理论法和投影寻踪法3种不确定性方法进行比较。在部分断面评价结果存在分歧时，本模型的水质分类结果与水质监测数据符合得最好，表明模型的评价结果更符合实际情况。

In order to better deal with the uncertain information in the process of water quality evaluation, the theory of set pair analysis is used. Considering the water quality classification level refinement, we divide the diversity factor in set pair analysis theory into good-difference and bad-difference and divide the reverse factor into good-reverse and bad-reverse. The weight of the index is calculated by the combining weighting method and corrected by considering the influence of stochastic observation error. The difference coefficient and correlation are used to optimize the index system. This model is applied in the evaluation of ground water functional zone in Yixing. The results are compared with those obtained using the artificial neural network, gray theory, and projection pursuit methods. When the evaluation results of the four methods are different in some sections, we find that the results using this model are in the best agreement with the water quality monitoring data.