Lymphoma　is　a　malignant　tumor　originating　from　the　lymphohematopoietic　system.　At　present,　pathological　evaluation　is　one　of　the　important　methods　to　diagnose　malignant　lymphoma.　In　clinical　practice,　the　diagnosis　of　lymphoma,　especially　in　newly　diagnosed　patients,　depends　mainly　on　histopathological　examination　of　the　lesion.　The　type　of　lymphoma　is　determined　by　repeatedly　comparing　hematoxylin-eosin　(H&E)　whole　slide　images　(WSIs)　and　immunohistochemical　WSIs　under　a　microscope.　It　is　a　repetitive,　tedious,　and　time-consuming　process.　Therefore,　it　is　extremely　important　to　establish　a　highly　accurate　and　standardized　lymphoma　diagnosis　algorithm.　In　this　paper,　we　developed　an　innovative　deep　-learning　framework　based　on　multi　-model　fusion,　which　only　uses　the　H&E　slides,　with　special　attention　to　gastric　Mucosa-associated　lymphoid　tissue　(MALT)　lymphoma　diagnosis.　The　proposed　framework　can　evaluate　and　improve　the　auxiliary　ability　of　the　convolutional　neural　network　(CNN)　in　clinical　practice　for　the　diagnosis　of　gastric　MALT　lymphoma.　The　proposed　method　achieved　an　accuracy　of　98.53%　using　image　patches　and　an　accuracy　of　94.96%　on　258　WSIs.　These　results　show　the　high　accuracy　in　the　diagnosis　of　MALT　lymphoma　and　its　potential　use　in　clinical　practice.　In　addition,　we　also　estimated　the　95%　confidence　interval　of　WSIs　prediction　values.　The　result　shows　that　the　proposed　framework　has　a　high　degree　of　differentiation　in　the　interpretation　between　gastric　MALT　lymphoma　and　normal　pathological　tissues.