为预测和提高聚丙烯/氢氧化镁（PP/MH）复合材料的阻燃性能，掌握不同因素对材料阻燃性能的影响强度，以MH粒径、接触角、添加量为3个输入量，以PP/MH复合材料的极限氧指数（LOI）为输出量，建立3层BP神经网络预测模型，将正交试验结果作为样本对其进行训练，用于预测复合材料的阻燃性能，设计实验对预测结果进行验证。结果表明：各因素对材料阻燃性能的影响由大到小依次为MH添加量、MH接触角和MH粒径。最佳的工艺参数：MH粒径为0.2 μm、MH接触角为135°、MH添加量为40%，此条件下PP/MH复合材料的LOI高达31.5%。该BP神经网络模型能够准确预测复合材料的阻燃性能，预测值和试验值的相对误差一般小于5%。建立的阻燃性能预测模型可用于材料的性能优化，可减少实验工作量，提高工作效率。

In order to improve the flame retardancy of polypropylene/magnesium hydroxide (PP/MH) composites and to grasp the intensity of different influencing factors on the flame retardancy of the materials, the MH particle size, contact angle, and the amount of additive were used as the three inputs, and the limiting oxygen index (LOI) of PP/MH composites was taken as the output. A three-layer BP neural network prediction model was established, and the orthogonal test results were used as samples to train it to predict the flame retardancy of the composites. The prediction results were verified by experiments. The results show that the effects of various factors on the flame retardancy of the PP/MH composites from large to small are MH content, MH contact angle and MH particle size. The optimal process parameters: the MH particle size is 0.2 µm, the MH contact angle is 135°, the MH content is 40%. Under these conditions, the LOI of PP/MH composite is as high as 31.5%. The BP neural network model can accurately predict the flame retardancy of composites, and the relative error between the predicted value and the experimental value is generally less than 5%. The prediction model of flame retardancy can be used to optimize the performance of materials, reduce the experimental workload, and improve the work efficiency.