Few-shot　semantic　segmentation　aims　to　segment　new　objects　in　the　image　with　limited　annotations.　Typically,　in　metric-based　few-shot　learning,　the　expression　of　categories　is　obtained　by　averaging　global　support　object　information.　However,　a　single　prototype　cannot　accurately　describe　a　category.　Meanwhile,　simple　foreground　averaging　operations　also　ignore　the　dependencies　between　objects　and　their　surroundings.　In　this　paper,　we　propose　a　novel　Transformer-based　Prototype　Search　Network　(TPSN)　for　few-shot　segmentation.　We　use　the　transformer　encoder　to　integrate　information　between　different　image　regions　and　then　use　the　decoder　to　express　a　category　in　terms　of　multiple　prototypes.　The　multi-prototype　approach　can　effectively　alleviate　the　feature　fluctuation　caused　by　limited　annotation　data.　Moreover,　we　use　adaptive　prototype　search　during　multi-prototype　extraction　instead　of　the　ordinary　averaging　operation　compared　with　the　previous　few-shot　prototype　framework.　This　helps　the　network　integrate　the　different　image　regions＇　information　and　fuse　object　features　with　their　dependent　background　information,　obtaining　more　reasonable　prototype　expressions.　In　addition,　to　encourage　the　category＇s　prototypes　to　focus　on　different　parts　and　maintain　consistency　in　high-level　semantics,　we　use　the　diversity　and　consistency　loss　to　constrain　the　multi-prototype　training.　Experiments　show　that　our　algorithm　achieves　state-of-the-art　performance　in　few-shot　segmentation　on　two　datasets:　PASCAL-5(i)　and　COCO-20(i).