Lake eutrophication is an important aspect of water quality assessment. The traditional grey target model is a valid method for evaluating lake eutrophication, but because of the correlation among assessment indicators, its predictive accuracy is questionable. To eliminate this influence, the FCM algorithm and improved grey target theory are introduced to evaluate the lake eutrophication. First, the FCM algorithm is applied to determine the weight coefficients; second, for the suggested model, the correlation coefficient matrix is used to replace the covariance matrix in the Mahalanobis distance; third, the positive and negative ideal solutions in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model are used to define the positive and negative target centers. Finally, the engineering example is verified using the improved model. Conclusions are drawn that the method is feasible for estimating the lake eutrophication, that its predictive accuracy is higher than that of traditional models, and that it can mine and develop data to the greatest extent using information from a few samples and limited information. These conclusions can provide a new approach and perspective for assessing lake eutrophication in the future.