Given　the　crucial　role　of　thiols　in　maintaining　normal　physiological　functions,　it　is　essential　to　establish　a　high-throughput　and　sensitive　analytical　method　to　identify　and　quantify　various　thiols　accurately.　Inspired　by　the　iron　porphyrin　active　center　of　natural　horseradish　peroxidase　(HRP),　we　designed　and　synthesized　two　iron　porphyrin　covalent　organic　frameworks　(Fe-COF-H　and　Fe-COF-OH)　with　notable　peroxidase-like　(POD)　activity,　capable　of　catalyzing　3,3　＇,5,5　＇-tetramethylbenzidine　(TMB)　into　oxidized　TMB　with　three　distinct　absorption　peaks.　Based　on　these,　a　six-channel　nanozyme　colorimetric　sensor　array　was　constructed,　which　could　map　the　specific　fingerprints　of　various　thiols.　Subsequently,　machine　learning　techniques,　including　supervised　learning　with　linear　discriminant　analysis　(LDA),　decision　trees　(DT)　and　artificial　neural　networks　(ANN),　unsupervised　learning　with　hierarchical　cluster　analysis　(HCA),　and　ensemble　learning　with　random　forests　(RF),　were　used　for　precise　identification　of　thiols　in　complex　systems,　with　a　detection　limit　as　low　as　50　nM.　Significantly,　the　sensor　array　demonstrated　strong　potential　for　practical　applications,　including　analyzing　homocysteine　(Hcy)　in　human　serum,　mercaptoacetic　acid　(TGA)　in　depilatory　creams,　and　glutathione　(GSH)　in　cell　lysates,　thereby　showing　promise　for　use　in　disease　diagnosis.