In　area　environment,　variations　of　life　form　can　exhale　difference　kind　of　audio　signals,　and　these　audio　signals　are　nearly　correlative　with　different　biologic　subsistence　conditions　and　human　activities.　To　analyze　these　audio　signals　automatically,　in　this　paper,　we　propose　a　method,　which　employ　effective　segment　length　of　audio　data　(ESLOAD),　frequency　component　of　maximum　harmonic　weight　(FCOMHW)　in　the　segment　and　first　order　difference　Mel-frequency　cepstral　coefficients　matrix　(D-MFCCM)　to　classify　area　environmental　audio　data.　In　the　method,　it　is　used　for　short-time　average　magnitude　to　effectively　segment　the　audio　data,　firstly;　then　FFT　to　calculate　FCOMHW　in　the　segment;　finally,　calculate　D-MFCCM.　For　classifying　operation,　according　to　ESLOAD　and　FCOMHW,　we　confirm　the　searching　range　of　every　segment,　individually　confirm　the　segment　possible　audio　type　with　corresponding　D-MFCCM,　and　then　confirm　the　possible　audio　types.　Through　the　experiment　of　9　categories　total　107　normal　area　environmental　audio　data,　it　is　indicated　the　method　is　effective　to　classify　area　environmental　audio　data.　©　2010　IEEE.