针对现有防腐涂料研发成本高、研发效率低的问题，通过数据输入、特征选择、测试集比例选择、机器学习算法种类选择、模型训练 /评估等步骤，训练出可进行耐老化性能预测的人工智能（AI）模型。研究了不同模型参数对 AI模型预测效果的影响，得到了适宜的模型参数。在 AI模型复用及验证的过程中，创新性地采用 “AI预测 +正交设计”的方法进行新型防腐配方优化设计和验证，相较基于经验试错法、正交试验法，采用新方法所需的实验量分别减少 93%、78%，显著提升新型涂料研发效率，降低研发成本，有助于解决材料智能研发面临的“小样本”难题。

In response to the problems of high research and development costs and low research and development efficiency of existing anti-corrosion coatings，an artificial intelligence（AI）model capable of predicting aging resistance performance was trained throughsteps such as data input，feature selection，test set ratio selection，machine learning algorithm type selection，and model training/evaluation. We studied the influence of different model parameters on the prediction performance of AI models and obtained suitable model parameters. In the process of reusing and validating AI models，an innovative approach of “artificial intelligence prediction + orthogonal design”was adopted for the optimization designand validation of new anti-corrosion formulas. Compared with the research methods based onempirical trial and error and orthogonal experiment，the experimental requirements of the newmethod were reduced by 93% and 78% respectively，significantly improving the efficiency of new coating research and development，reducing research and development costs，and helpingto solve the "small sample" problem faced by intelligent material research and development.