Data mining has been attached great importance in information industry. The main reason is that data mining stores lots of data which are broadly applicable. Besides, these data are urgently required to be transformed into useful information and knowledge. This paper mainly concerns a sorted and branching problem of data mining and designed an ensemble KNN classifier based on distance learning. This classifier firstly performed filtering uncorrelated attributes in data sets based on information gain and it filters redundant attributes with lower correlation degree.