The　zero-shot　semantic　segmentation　requires　models　with　a　strong　image　understanding　ability.　The　majority　of　current　solutions　are　based　on　direct　mapping　or　generation.　These　schemes　are　effective　in　dealing　with　the　zero-shot　recognition,　but　they　cannot　fully　transfer　the　visual　dependence　between　objects　in　more　complex　scenarios　of　semantic　segmentation.　More　importantly,　the　predicted　results　become　seriously　biased　to　the　seen-category　in　the　training　set,　which　makes　it　difficult　to　accurately　recognize　the　unseen-category.　In　view　of　the　above　two　problems,　we　propose　a　novel　zero-shot　semantic　segmentation　model　based　on　meta-learning.　It　is　observed　that　the　pure　semantic　space　expression　has　certain　limitations　for　the　zero-shot　learning.　Therefore,　based　on　the　original　semantic　migration,　we　first　migrate　the　shared　information　in　the　visual　space　by　adding　a　context-module,　and　then　migrate　it　in　the　visual　and　semantic　dual　space.　At　the　same　time,　in　order　to　solve　the　problem　of　biasness,　we　improve　the　adaptability　of　the　model　parameters　by　adjusting　the　parameters　of　the　dual-space　through　the　meta-learning,　so　that　it　can　successfully　complete　the　segmentation　even　in　the　face　of　new　categories　without　reference　samples.　Experiments　show　that　our　algorithm　outperforms　the　existing　best　methods　in　the　zero-shot　segmentation　on　three　datasets　of　Pascal-VOC　2012,　Pascal-Context　and　Coco-stuff.　(c)　2021　Published　by　Elsevier　B.V.