本文基于光学相关与神经网络学习算法和编码聚类原理的融合 ,提出一种大数量目标的高效率分类识别方法 ,并以 67个字符 ,其中包括2 6个英文字母、1 0个数字和 31个省和直辖市的简称汉字的分类识别作为实例 ,给出详细的原理说明和结果。 Based on the fusion of optical correlation and neural network learning algorithm and coding clustering theory, this paper proposes a high-efficiency classification and identification method for a large number of targets, and uses 67 characters, including 26 English letters, 10 numbers and 31 Provinces and municipalities referred to as the classification of Chinese characters as an example, gives a detailed description of the principles and results.