A new method of machine learning based on examples is given in this paper. This method improves the classical method ID3 which learns from static examples. Its limits lie on no comprehension and no memory, and no dynamic correlation. In the new method, it can learn from dynamic examples, the change of data can be learned because the training data is the initial and end process in the interval. All varieties and correlation can be understood and remembered. By experiments, the method can be used as classifier and it has special use in the field of information mining.