In the big data environment， the continuous growth of data in the ordered decision information system leads to the dynamic change of the dominance relationship between objects. Efficient calculation of attribute reduction has become a key problem to be solved urgently. Therefore， an incremental single-valued medium-intelligence dominance conditional entropy is proposed， and a new incremental attribute reduction algorithm is constructed accordingly. Firstly， the single-valued medium-intelligence dominance conditional entropy is given under the single-valued medium-intelligence ordered decision information system. Subsequently， for four different types of new objects， the incremental update mechanism of single-valued medium-intelligence dominance conditional entropy is deeply studied， and then an incremental attribute reduction algorithm is designed according to this update mechanism. Finally， six UCI datasets with dominance relations are selected to conduct a comparative experimental analysis on the effectiveness and efficiency of the incremental algorithm and the non-incremental algorithm. Experimental results show that the newly given incremental attribute reduction algorithm can significantly improve the computational efficiency of data processing while maintaining the same classification accuracy.