Abstract: Taking chemical oxygen demand, total nitrogen, total phosphorus and transparency as artificial neural network evaluation parameters and after repeated attempts.the four-layer structural Error Back Propagation Network(EBPN)was established to evaluate lake eutrophication.There are four neural units in input layer, four in both hidden layers, and one in output layer.Taking the eutrophication evaluation criterion of Taihu Lake as sample pattern, the network was trained in the light of learning rule of EBPN.After 37684 tries, the network reached the convergence standard given in advance, enabling it to possess the function of distinguishing the degree of lake eutrophication.This network was used to evaluate the eutrophication degree of 17 lakes in China.Its operation process was simple and convenient, and the results accorded with reality, showing that the approach has a series of advantages.

Key words: Artificial neural network, Lake eutrophication, Evaluation