To evaluate and predict the strength of backfilling body, a new method is provided to establish a model of the relationship between backfilling body strength and influence factors. The modified BP neural network algorithm is used to establish the model based on 18 groups results of the compressive strength tests of the backfilling in laboratory. The structure of the model is 5-7-1 type, that is to say 5,7 and 1 neurons are the input, hidden and output layers respectively, where the input is including the cement-sand ratio and quantity of the cemented material and the output is the 28 days compressive strength of the backfilling body. The results show that BP neural network model exhibits excellent prediction ability for the prediction of the strength of backfilling body, the prediction model speeds up the convergence rate of BP network and improves the training accuracy. The maximum relative error between the predicted results and the test data is 4.23％.