Concerning of the curse of dimensionality caused by too high data dimension and redundant information， a high-dimensional Attribute Reduction algorithm based on Similarity and Difference Matrix （ARSDM） was proposed. In this algorithm， on the basis of discernibility matrix， the similarity measure for samples in the same class was added to form a comprehensive evaluation of all samples. Firstly， the distances of samples under each attribute were calculated， and the similarity of same class and the difference of different classes were obtained based on these distances. Secondly， a similarity and difference matrix was established to form an evaluation of the entire dataset. Finally， attribute reduction was performed， i.e.， each column of the similarity and difference matrix was summed， the feature with the largest value was selected into the reduction in proper order， and the row vector of the corresponding sample pair was set to the zero vector. Experimental results show that compared with the classical attribute reduction algorithms DMG （Discernibility Matrix based on Graph theory）， FFRS （Fitting Fuzzy Rough Sets） and GBNRS （Granular Ball Neighborhood Rough Sets）， the average classification accuracy of ARSDM is increased by 1.07， 6.48， and 8.92 percentage points respectively under the Classification And Regression Tree （CART） classifier， and increased by 1.96， 11.96， and 12.39 percentage points under the Support Vector Machine （SVM） classifier. At the same time， ARSDM outperforms GBNRS and FFRS in running efficiency. It can be seen that ARSDM can effectively remove redundant information and improve the classification accuracy.
