Under the condition of insufficiency of the training sets, Bayesian will easily make the classification of the new incremental and unlabeled training texts incorrectly. If these incorrectly labeled texts are added to the Bayesian classifier early, it will reduce the performance of Bayesian classifier. In addition, incremental learning with fixed confidence level parameter will cause low learning efficiency and instable generalization ability. In order to solve the above problems, this paper proposes an incremental learning method of dynamic confidence level and sequence selectable. Firstly, the new incremental training subsets are made up of these texts which are classified by current Bayesian classifier correctly. Secondly, it uses confidence level to dynamically monitor the performance of classifier, and then chooses texts from the new incremental training subsets. Finally, strengthen the positive impact of the more mature data, weaken the negative impact of the noise data, and complete the text classification of the test sets by choosing reasonable learning sequence. The experimental results show that the classification efficiency and precision are both advanced by using the method this paper proposes.