摘要： 针对向量空间模型的网页分类计算复杂度高、不适用于大规模场景问题，该文采用RSOM和BAYES相结合的方法实现网页分类，利用RSOM 神经网络树实现网页特征词的自动索引，利用Bayes实现网页的自动分类。结果证明其在特征空间维数、检索效率、样本容量及检索精度方面都具有良好的性能。

关键词: 网页分类, RSOM神经网络树, Bayes方法, 向量空间模型

Abstract: Most Web page classification methods are based on Vector Space Model(VSM), but it is not suitable for large scale application background with bad computation complexity. A new automated text classification method based on RSOM neural net tree and Bayes method is proposed, RSOM neural net tree is used in Web page index and Bayes method is used in automated Web page classification. The excellent performance of this method has been tested in feature dimension, performance, capacity and accuracy.

Key words: Web page classification, RSOM neural net tree, Bayes method, Vector Space Model(VSM)

中图分类号: 

• TP391