There are three problems in rule set which is extracted based on classification algorithm.First, too few short rules in the extracted classification rule set decrease the number of high quality rules. Second, there are such few rules in rule set that almost all of the examples in the training data can be covered only once.Third, despite lots of extracted rules, some examples of small classes in the training data fail to be covered by any of these rules. Herein, a modified example multiple coverage classification algorithm RCIM, which is based on generated rules, is proposed. Here are the features: (1) for the purpose of improving the quality of rules, not only the quality of attribute value but also that of its complement can be taken into account when choosing the first item of a generated rule. (2) It can generate high quality rules at a time as many as possible. (3) It deletes the examples in the training data only if they are covered at least twice.What′s more, it can restudy each of the attribute value of the test data to extract rules when encountering the data difficult to judge.The algorithm RCIM not only can efficiently extract a large quantity of rules but also largely improve the quality of rules. Experimental results in many data show that RCIM has achieved higher classification accuracy than many other algorithms.