Abstract: In order to solve the problem of Deep Web data sources classification, this paper firstly researched how fealures in different position could effect the domain of Deep Web interfaces, and proposed a feature selection method RankF W which is based on Ranked weights. Then, a quantum self-organization feature mapping network model was proposed with a classification algorithm This model relies on the feature vectors and target vectors incoordinately in different phases of training, making a more centralized distribution of winner neurons in competition layer and more obvious boundaries among clusters. Finally, some experiments were designed and carried out on the expanded TEL-8 dataset to test the validity of RankFW and DR-QSOFM.

Key words: Decp Web interface, Feature selection, Topic classification, Ranked weight, Domain relied, Quantum self-or-ganization feature mapping