Abstract: Temporal networks could describe the evolution characteristics of complex systems more accurately by considering the sequence of events. In this paper, the multi-attribute sorting method (TOPSIS) is introduced to comprehensively evaluate the influence of different time slices of temporal networks. By calculating the Euclidean distance of different inter-layer coupling indexes to the positive-ideal solution and the negative-ideal solution, this method ranks the indexes according to the measurement that the results are close to the positive-ideal solution and far away from the negative-ideal solution. The relevant experiments on Workspace datasets show that the Preferential Attachment Index (PA) to measure the temporal coupling relationship can dig out the highest accuracy, the average of 50.82% on each layer. Our work may shed some lights for analyzing temporal networks from multi-attribute perspective.