针对全方位的危化品运输企业安全要素建立了基于BP神经网络的评价模型,采用基于最速下降法的权值学习算法进行权值修正,使得评价方法具有快速、准确的特点。用样本数据训练了神经网络评价模型,测试结果显示评价模型自我调节能力强,精度较高,适合受随机评价因素影响较重的危化企业安全评价应用。 The evaluation model based on BP neural network is established for all the safety elements of hazardous chemicals transportation enterprises. Weighting algorithm based on the steepest descent method is used to modify the weight, which makes the evaluation method fast and accurate. The sample data are used to train the neural network evaluation model. The test results show that the evaluation model has strong self-regulation ability and high accuracy, and is suitable for the application of safety evaluation of the enterprises that are seriously affected by random evaluation factors.