In recent years, with the increasing amount of data in real life, inconsistent data becomes more frequent. This makes manual correction of inconsistent data more time-consuming. Moreover, manual correction prone to human errors. Thus, such correction method is no longer feasible. How to perform classification directly on inconsistent data without correcting data beforehand is the core research content of this paper. In this paper, the objective function of the decision tree generation algorithm is improved so that it can directly classify inconsistent data and achieve better results. Multidimensional measures of the influence of the feature are used on classification results to adjust the influence factor of the feature so that nodes of the decision tree can be split more accurate to achieve more effective classification results.