目的 应对快速多变的市场，提前预知市场发展，制定相应的排产计划，使企业在竞争中占据先发优势。方法 目前基于灰色神经网络的预测算法，准确地预测产品需求通常需要连续且大量的样本数据，对小数据非线性系统的预测结果精确度低、可靠性差，针对这一问题，提出一种耦合遗传算法的灰色神经网络预测方法，综合灰色模型和神经网络理论，构建了面向产品订单量需求预测的灰色神经网络模型;通过电力机车产品实例分析了模型的预测性能;为解决预测过程中模型早熟收敛的问题，利用遗传算法对训练网络的权重和阈值进行了迭代优化。结论 研究结果表明，优化后产品预测模型的精确性和鲁棒性得到提高，验证了所设计方法的可行性。

In order to cope with the quicken pace of market, predict the market development in advance and formulate corresponding production scheduling plans, and enable companies to occupy a first-mover advantage in competition. The current forecasting algorithm based on gray neural network usually requires continuous and much sample data to accurately predict product demand. Aiming at the problem of low accuracy and poor reliability of prediction results of small data nonlinear systems, a grey neural network prediction method coupled with genetic algorithm is proposed. Firstly, the gray neural network model for forecasting product order demand is established based on the gray model and neural network. Secondly, the electric locomotive product is taken as an example to demonstrate the prediction performance of the model. Lastly, the genetic algorithm is used to iteratively optimize the network weights and thresholds of the mode to solve the premature convergence and improve the global optimization capability in the prediction process. The results show that the accuracy and robustness of the optimized product prediction model are improved, which verifies the feasibility of the designed method.