Faced with the ever-increasing amount of books published,the library cataloging staff feel more and more difficult to classify books manually.How to realize automatic book classification by using computers is a critical question in digital library construction.This paper attempts to introduce the machine learning algorithms,such as BP neural network and Support Vector Machine,into the practice of book classification.A machine learning based hierarchical book classification system model is estabUshed according to the Chinese Library Classification.Related design approaches are proposed to describe a bibliography by using characteristic weighting method and the focus is on the construction of the shallow classification systems.Large-scale experiment is conducted to justify the feasibility and appropriateness of this model.This research basically solves the problem of automatically classifying books without subject indexing. Faced with the ever-increasing amount of books published, the library cataloging staff feel more and more difficult to classify books manually.How to realize automatic book classification by using computers is a critical question in digital library construction.This paper attempts to introduce the machine learning algorithms, such as BP neural network and Support Vector Machine, into the practice of book classification. A machine learning based hierarchical book classification system model is estabUshed according to the Chinese Library Classification. Abstract design approaches are presented to describe a bibliography by using characteristic weighting method and the focus is on the construction of the shallow classification systems. Large-scale experiment is conducted to justify the feasibility and appropriateness of this model. This research substantially solves the problem of automatically classifying books without subject indexing.