Abstract: The multi-attribute ranking method based on Euclidean distance (TOPSIS-ED) can evaluate researcher academic influence through taking into account the different attributes of researchers. However, the method also has a problem that the points on the vertical line cannot be sorted. In this paper, we propose a multi-attribute ranking method based on relative entropy (TOPSIS-RE) by considering five indicators including the total number of papers, the total number of citations, citations per papers, I10-index and H-index. By calculating the relative entropy of the five indicators to the positive-ideal solution and the negative-ideal solution, this method ranks the authors according to the measurement that the results are close to the positive-ideal solution and far away from the negative-ideal solution. We select the American Physical Society data set as the training set and the authors who have won the Nobel Prize in the American Physical Society data set as the testing set. The area under curve (AUC) value is used to illustrate the accuracy of the algorithm. The results show that the AUC value calculated by TOPSIS-RE is 0.9321, and increases by 2.047% and 0.833% respectively compared with the total number of citations and TOPSIS-ED. Our work may shed some lights for quantifying the influence of scientists from the multi-attribute perspective.