Engineering　photoelectrodes　with　cocatalysts　is　an　effective　approach　to　enhance　their　photocatalytic　performance.　However,　selecting　an　appropriate　cocatalyst　for　a　specific　photoelectrode　is　challenging　due　to　various　factors　within　the　system.　Machine　learning　techniques　are　revolutionizing　the　catalytic　field　and　are　good　at　addressing　multifactorial　problems.　In　this　study,　several　fundamental　factors　of　the　photoanode　catalytic　system　and　their　specific　mechanisms　were　systematically　summarized.　A　comprehensive　machine　learning　process　was　introduced　to　guide　cocatalyst　selection　for　BiVO4　photoanodes.　A　multi-layer　perceptron　neural　network　and　several　tree-based　ensemble　models　were　trained　to　capture　intricate　relationships　among　photoanodes,　cocatalysts,　and　electrolytes,　enabling　the　prediction　of　unstudied　cases.　The　best-performing　random　forest　model　accurately　captured　the　essential　features　of　high-performance　cases,　achieving　a　generalization　accuracy　of　96.30%　for　binary　classification.　Furthermore,　the　model＇s　built-in　feature　importance　analysis　revealed　that　the　type　and　preparation　method　of　cocatalysts　were　the　two　most　significant　factors　affecting　the　catalytic　system＇s　performance.　According　to　the　Shapley　additive　explanations　interpretation,　some　heuristic　rules　were　provided　to　propose　a　class　of　promising　cocatalyst/photoanode　catalysts.　©　2023　The　Royal　Society　of　Chemistry.