Abstract: With the application of the unclassified tailing cemented filling in a mine，the water reducing agent is added to ensure the flow property of filling slurry.There exists more complex chemical-physical effect among various influencing factors of filling body strength.In order to grasp the effect law of various factors on the strength of filling body，and predict its strength accurately，the backfilling strength of cementing filling body prediction model is set up based on the BP neural network of orthogonal experiment.In the forecasting process，three factors and four levels orthogonal experiment scheme is designed as the research data base，considering slurry concentration，cement-sand ratio，water reducing agent adding proportion as the influencing factors，to improve the sample scientific credibility.The grey relational theory is used to analyze the effect law of every factors on the backfilling filling body：the biggest impact on the early strength of filling body (7 d) is slurry concentration，followed by cement-sand ratio，water reducing agent proportion.The late strength of filling body (28 d) is most affected by cement-sand ratio，then followed by slurry concentration，water reducing agent proportion.For purpose of predicting the strength of the backfilling filling body exactly，it regards 7 d，28 d strength as the output factor，and adopts BP neural network，and as a result the maximum relative error is 9.98%，the average relative error is 2.71%，which meets the requirements of filling body strength prediction，and has a good application value to the project.

Key words: Unclassified tailing, Water reducing agent, Orthogonal experiment, Backfilling filling body, Srength prediction