As a typical classification method， support vector machine (SVM) has been widely used in various fields. However， the standard SVM faces the following problems in the classification decision: First， it does not consider the distribution characteristics of the classification data; Second， it ignores the relative relationship between sample categories； Third， it can not solve the problem of large-scale classification. In view of this， the rank preservation learning machine based on data distribution fusion (RPLM-DDF) is proposed， in which within-class scatter is introduced to describe the distribution properties， and through the relatively constant position of all kinds of sample data centers， the global sample order remains unchanged. The large-scale classification problem is solved by certifying RPLM-DDF and the duality of the core vector machine. The comparison experiments on the artificial datasets， small-scale datasets and large-scale datasets verity the effectiveness of the RPLM-DDF.