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.