中文摘要：

为了提高文本自动分类准确率，提出一种改进的蜂群优化神经网络的选择特征的文本数据挖掘算法。该算法将文本特征选择转换成一个多目标优化问题，以特征维数最少、分类正确率最高为选择标准，采用蚁群算法找到最优特征子集，最后神经网络建立文本自动分类器，进行仿真实验测试算法性能。仿真实验结果表明，提出的方法从高维文本最优文本特征，提高了文本自动分类的正确率和识别效率，-是一种有效的网络文本挖掘算法。

英文摘要：

In order to improve text classification accuracy, this paper proposed a text automatic categorization method based on ant colony algorithm and neural network. Firstly, it transformed the text feature selection into a multi-objective optimization problem, and took the feature dimension and the correct rate as a feature selection evaluation criteria, and used ant colony al- gorithm to solve the multi objective optimization problems to find the optimal feature subset, and then optimized the parameters of neural network by ant colony algorithm to establish the optimal text categorizer. The results show that the simulation experi- ment arry out on the text data, this proposed method not only can quickly find the optimal feature subset of text, but also im- proves the classification accuracy and efficiency.