The　realization　of　Internet　of　Things　(IoT)　traffic　classification　is　crucial　to　the　management　and　monitoring　of　the　IoT　network.　Performing　IoT　traffic　classification　under　a　few-shot　scenario　is　vital　due　to　extremely　rapid　growth　in　the　number　of　IoT　devices　and　necessity　to　save　computational　costs.　In　this　paper,　we　propose　MAFFIT,　a　multi-perspective　feature　approach　to　few-shot　classification　of　IoT　traffic.　The　purpose　is　to　comprehensively　consider　the　information　of　network　traffic　and　to　achieve　accurate　classification　of　IoT　traffic　using　a　limited　number　of　samples.　MAFFIT　is　based　on　our　key　observation　that　traffic　behaviour　and　traffic　composition　are　highly　consistent　across　IoT　traffic　of　the　same　class.　For　a　flow,　MAFFIT　will　first　extract　the　packet　length　sequences　and　packet　byte　sequence,　then　encodes　the　features　of　the　corresponding　sequences　using　feature　construction,　and　finally　uses　comparative　learning　to　obtain　the　class　of　the　flow　without　the　additional　cost　of　training　a　comparison　model.　We　conduct　extensive　experiments　on　two　real-world　IoT　traffic　datasets,　the　results　demonstrate　that　MAFFIT　can　achieve　accurate　IoT　traffic　classification　using　a　limited　number　of　flow　samples　and　MAFFIT　outperforms　three　existing　network　traffic　classification　methods.