In order to investigate the impact of whole tailing characteristics of size grading on the performance of backfilling material，this study selected fractional dimension number and correlation coefficient of fractional dimension number to characterize the geometric features of whole tailing.By using cement-sand ratio，slurry concentration，fractional dimension number and correlation coefficient of fractional dimension number as the input factors，compressive strength，slump and bleeding rate as the output factors，a fractal-BP neural network model was constructed to predict the properties of backfilling material.Then data of 7 mines were calculated by the fractal dimension and correlation coefficient of fractal dimension，and the BP neural network was used for the training and prediction.The results showed that the finer the tailing，the bigger the size grading fractional dimension，but contrary to the pore fractal dimension.Furthermore，the fractional dimension of whole tailing is a little higher than grading tailing.The correlation coefficient of the grading tailing is between 0.71 to 0.97，which is more dispersed than that of whole tailing.The relative error is under 8% using fractal-BP neural network model to predict the properties of backfilling material.In a conclusion，the fractal-BP neural network model had a fine precision，which provides a new approach to predict the properties of filling material.