Abstract: In order to improve the accuracy and scientificity of the prediction of stress corrosion cracking (SCC) sensitivity of stainless steel, the main influencing factors of SCC behavior of stainless steel were extracted by principal component analysis (PCA) as the input of the subsequent model, and then the representative algorithms of different schools of machine learning were mixed into Hybrid model, and optimized by particle swarm optimization (PSO) algorithm, and the prediction model PSO-Hybrid of SCC sensitivity of stainless steel was proposed. Taking the measured data of an austenitic stainless steel as an example, the predicted value and the actual value were compared to verify the reliability and superiority of the model. The results showed that the Hybrid idea was feasible and scientific, and after PSO optimization, the average accuracy of the Hybrid model and the Matthews correlation coefficient were increased by 3.3% and 8.3% respectively. The PSO-Hybrid model had high prediction accuracy and good stability.