Since 1990?s, the exponential growth of these Web documents has led to a great deal of interest in developing efficient tools and software to assist users in finding relevant information. Text classification has been proved to be useful in helping organize and search text information on the Web. Although there have been existed a number of text classification algorithms, most of them are either inefficient or too complex. In this paper we present two Odds-Radio-Based text classification algorithms, which are called OR and TF*OR respectively. We have evaluated our algorithm on two text collections and compared it against k-NN and SVM. Experimental results show that OR and TF*OR are competitive with k-NN and SVM. Furthermore, OR and TF*OR is much simpler and faster than them. The results also indicate that it is not TF but relevance factors derived from Odds Radio that play the decisive role in document categorization.