Music　is　a　series　of　harmonious　sounds　well　arranged　by　musical　elements　including　rhythm,　melody,　and　harmony　(RMH).　Since　music　digitalization　has　resulted　in　a　wide　variety　of　new　musical　applications　used　in　daily　life,　the　use　of　music　genre　classification　(MGC),　especially　MGC　automation,　is　increasingly　playing　a　key　role　in　the　development　of　novel　musical　services.　However,　achieving　satisfactory　performance　of　MGC　automation　is　a　practical　challenge.　This　paper　proposes　a　two-step　approach　for　music　genre　classification　(called　TSMGC)　on　the　strength　of　analytic　hierarchy　process　(AHP)　weighted　musical　features.　Compared　with　other　MGC　approaches,　the　TSMGC　has　three　strong　points　for　better　performance:　(1)　various　musical　features　extracted　from　the　RMH　and　the　calculated　entropy　are　comprehensively　considered,　(2)　the　weight　of　features　and　their　impact　values　determined　by　AHP　are　applied　on　the　basis　of　the　Exponential　Distribution　function,　(3)　music　can　be　accurately　categorized　into　a　main-class　and　further　sub-classes　through　a　two-step　classification　process.　According　to　the　conducted　experiment,　the　result　exhibits　an　accuracy　rate　of　87%,　which　demonstrates　the　potential　for　the　proposed　TSMGC　method　to　meet　the　emerging　needs　of　MGC　automation.