In　order　to　improve　the　training　speed　of　classifiers　without　losing　their　accuracy,　three　classifying　algorithms　based　on　information　gain　of　features　are　provided　in　this　work.　They　are　IG-C1,　IG-C2　and　IG-C,　which　classifies　unlabeled　text　according　to　features　weight　generated　in　feature　selection　phase.　All　these　approaches　have　two　characteristics:　lower　time　complexity　and　simpler　implementation.　The　performance　comparison　between　these　algorithms　and　Naive　Bayes,　Vector　Space　Model　using　retuers　21578　and　20　newsgroup　data　sets,　shows　that　IG-C　algorithm　is　best　one.