基于离散Hopfield神经网络联想记忆特性，建立了湖库富营养化等级综合评价模型，对全国24个湖库进行富营养化等级综合评价，并与文献投影寻踪法、评分指标法和LM-BP网络法的评价结果进行比较。结果表明：①离散Hopfield神经网络运用于湖库营养化等级评价具有简单、直观，容易实现等优点，其评价结果令人满意；②一般离散Hopfield神经网络并非适用于任何富营养化等级评价，当评价对象单项指标（因子）间存在较大差异时，对象将得不到正确的评价。

Based on the associative memory of discrete Hopfield neural network, a model to  comprehensively assess the eutrophication level of lakes and reservoirs is established. Twenty-four lakes and reservoirs in China are evaluated through this model, and the results are compared with those of  projection pursuit method,  score index method, and LM-BP network method. The results show that discrete Hopfield neural network is simple, intuitive, and easy to implement, with only a few  iterations leading to satisfactory and objective results. However, not all eutrophication level assessments could be achieved through general discrete Hopfield neural network. When there is a big difference between each single index (factor), correct assessment could not be achieved.