BACKGROUND:　Malignant　lymphoma　is　a　type　of　tumor　that　originated　from　the　lymphohematopoietic　system,　with　complex　etiology,　diverse　pathological　morphology,　and　classification.　It　takes　a　lot　of　time　and　energy　for　doctors　to　accurately　determine　the　type　of　lymphoma　by　observing　pathological　images.　OBJECTIVE:　At　present,　an　automatic　classification　technology　is　urgently　needed　to　assist　doctors　in　analyzing　the　type　of　lymphoma.　METHODS:　In　this　paper,　by　comparing　the　training　results　of　the　BP　neural　network　and　BP　neural　network　optimized　by　genetic　algorithm　(GA-BP),　adopts　a　deep　residual　neural　network　model　(ResNet-50),　with　374　lymphoma　pathology　images　as　the　experimental　data　set.　After　preprocessing　the　dataset　by　image　flipping,　color　transformation,　and　other　data　enhancement　methods,　the　data　set　is　input　into　the　ResNet-50　network　model,　and　finally　classified　by　the　softmax　layer.　RESULTS:　The　training　results　showed　that　the　classification　accuracy　was　98.63%.　By　comparing　the　classification　effect　of　GA-BP　and　BP　neural　network,　the　accuracy　of　the　network　model　proposed　in　this　paper　is　improved.　CONCLUSIONS:　The　network　model　can　provide　an　objective　basis　for　doctors　to　diagnose　lymphoma　types.