This　study　proposes　a　Bayesian　optimization-based　diagnostic　algorithm　for　convolutional　neural　networks　(CNN),　which　improves　the　predictive　performance　of　the　model　by　optimizing　the　hyperparameters　of　the　CNN.　Traditional　CNN　models　often　rely　on　manual　experience　to　adjust　the　hyperparameters　when　facing　complex　data,　which　is　not　only　time-consuming　but　also　difficult　to　ensure　finding　the　optimal　solution.　To　overcome　this　challenge,　this　paper　adopts　the　Bayesian　optimization　algorithm　to　automatically　adjust　the　hyperparameters　of　the　CNN.　Bayesian　optimization　significantly　improves　the　efficiency　of　hyperparameter　search　and　the　accuracy　of　the　model　by　constructing　an　agent　model　and　using　the　existing　experimental　data　to　speculate　the　next　optimal　hyperparameter　combination.　The　experimental　results　show　that　the　CNN　model　with　Bayesian　optimization　outperforms　the　standard　CNN　on　the　test　set,　especially　in　several　evaluation　indexes　such　as　precision,　recall　and　F1　score,　which　have　achieved　significant　improvement.　In　addition,　the　error　estimation　in　the　Bayesian　optimization　process　shows　that　the　estimated　value　of　the　minimum　error　is　gradually　close　to　the　actual　value　with　the　increase　of　the　number　of　iterations,　which　verifies　the　improvement　of　the　accuracy　of　Bayesian　optimization　in　the　optimization　process.　©　2025　IEEE.