Aiming at the problems of setting vigilance parameter and pattern drift produced in the process of classification identification of the traditional ART2 neural network,a new ART2 neural network model based on modified algorithm is presen-ted in this article to solve problems concerning analysis of pattern identification. Reasonable vigilance parameter needed by clus-tering is deduced through the processing of self-organization,weighting and iteration. In order to conduct reasonable classifica-tion of clustering objects,the measures of slowing learning rate which can be realized by modifying the weight training of ART2 neural network to reduce the speed of pattern drifting should be taken. The experimental results have proved that the new model is of high validity and feasibility.%针对传统的ART2神经网络中对于主观设置的警戒参数以及识别分类过程中产生模式漂移的问题，提出基于改进算法的ART2神经网络模型，用于解决分析模式识别问题。通过自组织，加权，迭代等过程推导合理的警戒参数用于聚类运算，通过对ART2神经网络的权值训练方面进行修正，减缓学习速率，降低模式漂移速度，近一步对聚类对象进行合理分类。实验结果证明，该方法是可行的和有效的。
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