Based on the associative memory of discrete Hopfield neural network, a model lo 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 eutrophica-tion 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.%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.
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