In　order　to　improve　the　detection　efficiency　of　Android　malicious　application,　an　Android　malware　detection　system　based　on　feature　fusion　is　proposed　on　three　levels.　Feature　fusion　especially　emphasizes　on　ten　categories,　which　combines　static　and　dynamic　features　and　includes　377　features　for　classification.　In　order　to　improve　the　accuracy　of　malware　detection,　attribute　subset　selection　and　principle　component　analysis　are　used　to　reduce　the　dimensionality　of　fusion　features.　Random　forest　is　used　for　classification.　In　the　experiment,　the　dataset　includes　43,822　benign　applications　and　8,454　malicious　applications.　The　method　can　achieve　99.4%　detection　accuracy　and　0.6%　false　positive　rate.　The　experimental　results　show　that　the　detection　method　can　improve　the　malware　detection　efficiency　in　Android　platform.