As　an　important　basic　task　in　natural　language　processing,　textual　entailment　recognition　has　practical　applications　in　QA,　information　retrieval　IR,　information　extraction　and　many　other　tasks.　The　traditional　methods　of　text　imputation　include　mainly　classification　methods　based　on　artificial　features,　methods　based　on　word　similarity,　and　so　on.　Traditional　methods　require　a　large　number　of　manual　extraction　features,　construction　rules,　and　the　like.　Deep　neural　networks　can　avoid　the　problems　of　manual　extraction　features　in　traditional　machine　learning　methods　and　error　accumulation　caused　by　NLP　preprocessing　tools.　With　the　good　results　of　deep　learning　in　the　field　of　natural　language　processing,　the　study　of　natural　language　reasoning　and　textual　inclusion　recognition　is　also　increasing.　The　same　text　under　different　tasks　will　focus　on　its　focus.　For　the　text　implied　recognition　task,　we　usually　focus　on　whether　the　sub-events　in　each　text　match.　If　we　can　decompose　a　sentence　into　sub-events　related　to　it,　and　judge　that　the　sub-events　in　the　hypothetical　text　are　contained　in　the　sub-events　of　the　premise　text,　then　we　can　determine　the　implication　between　the　two　sentences.　The　mLSTM　model　achieved　an　accuracy　of　86.1%　on　the　SNLI　corpus,　which　is　the　best　level　at　present,　but　it　is　not　effective　on　other　small　corpus.　This　article　aims　to　improve　the　mLSTM　model,　establishing　the　mGRU　model　based　on　GRU　(Gated　Recurrent　Unit),　and　verify　the　effect　of　the　model　on　SNLI　corpus　and　multiNLI　corpora.