Water quality evaluation is important because it could provide guidance when determining water utility. But many interacting impact factors are involved in water quality evaluation systems, making water quality evaluation difficult. Principal component analysis (PCA) is widely used in water quality evaluation because it can eliminate the correlation among factors. However, PCA ignores the degree of data dispersion, which is considered by information entropy (IE). To solve this problem, a model combined PCA and IE methods to obtain the weights of indicators is proposed in this paper, and the proposed model was applied to assess the reused water quality of Jinshui River in Zhengzhou City in 2009. The evaluation results were compared with those using PCA and IE methods for the same data. The results proved that the method is feasible and practical, and it can provide a theoretical basis and decision reference for the utility of unconventional water.