Recently,　there　are　various　methods　for　detecting　the　hardware　trojans　(HTs)　in　the　integrated　circuits　(ICs).　The　circuit＇s　logic　representations　of　different　types,　structures,　and　functional　characteristics　should　be　different.　Each　type　of　circuit　has　its＇　own　performance　characteristics　according　to　its＇　purpose.　However,　the　traditional　HTs　detection　methods　adopt　the　same　approach　to　deal　with　the　multi-purpose　hardware　trojan.　At　the　same　time,　as　the　scale　of　integrated　circuits　growing,　the　structures　are　more　complex,　and　the　functions　are　more　refined.　The　current　situation　makes　the　traditional　HTs　detection　methods　weaker　and　even　unfeasible.　Therefore,　we　propose　an　HTs　classified　locating　method　based　on　machine　learning,　named　ML-HTCL,　which　belongs　to　the　static　detection　and　locating　method.　In　ML-HTCL,　different　purpose　HTs　were　represented　by　different　features.　Due　to　the　different　features,　the　ML-HTCL　employs　the　multi-layer　BP　neural　network　for　the　control　signal　type　HTs　and　the　one-class　SVM　for　the　information　leakage　HTs,　respectively.　To　deal　with　the　HTs　completely,　the　boundary　nets　are　considered　for　all　data　set,　while　those　were　ignored　by　the　most　existing　methods　due　to　the　high　detection　error　rate.　After　detection,　the　HTs＇　precise　locations　were　achieved.　To　evaluate　the　ML-HTCL,　17　gate-level　netlist　benchmarks　are　used　for　training　and　testing　by　leave-one-out　cross-validation.　From　the　results,　the　ML-HTCL　reaches　85.05%　of　TPR　and　73.91%　of　TNR　for　all　kinds　of　HTs,　which　performs　better　than　the　most　existing　methods.