Meta-learning　is　one　of　the　latest　research　directions　in　machine　learning,　which　is　considered　to　be　one　of　the　most　probably　ways　to　realize　strong　artificial　intelligence.　Meta-learning　focuses　on　seeking　solutions　for　machines　to　learn　like　human　beings　do　-　to　recognize　things　through　only　few　sample　data　and　quickly　adapt　to　new　tasks.　Challenges　occur　in　how　to　train　an　efficient　machine　model　with　limited　labeled　data,　since　the　model　is　easily　over-fitted.　In　this　paper,　we　address　this　obvious　but　important　problem　and　propose　a　metric-based　metalearning　model,　which　combines　attention　mechanisms　and　ensemble　learning　method.　In　our　model,　we　first　design　a　dual　path　attention　module　which　considers　both　channel　attention　and　spatial　attention　module,　and　the　attention　modules　have　been　stacked　to　conduct　a　meta-learner　for　few　shot　meta-learning.　Then,　we　apply　an　ensemble　method　called　snap-shot　ensemble　to　the　attention-based　meta-learner　in　order　to　generate　more　models　in　a　single　episode.　Features　abstracted　from　the　models　are　put　into　the　metric-based　architecture　to　compute　a　prototype　for　each　class.　Our　proposed　method　intensifies　the　feature　extracting　ability　of　backbone　network　in　meta-learner　and　reduces　over-fitting　through　ensemble　learning　and　metric　learning　method.　Experimental　results　toward　several　meta-learning　datasets　show　that　our　approach　is　effective.