Histopathological　examination,　as　the　＂gold　standard＂　recognized　by　the　medical　community,　can　easily　reach　the　resolution　of　microns　and　is　a　qualitative　examination.　With　the　rapid　development　of　computer　hardware　and　software,　deep　learning　has　been　triggered　in　the　computer-aided　diagnosis　of　medical　images.　To　create　an　algorithm　to　identify　metastatic　cancer　in　small　image　patches　taken　from　larger　digital　pathology　scans,　we　proposed　a　hybrid　deep　learning　model　which　combining　Resnet　and　Densenet　We　combined　Resnet　and　Densenet　to　extract　image　features.　We　introduced　some　related　work　about　the　histopathological　examination.　In　the　experiment,　we　compared　our　model　with　other　models,　the　elevation　metrics　is　accuracy　and　Auc-Roc　score.　From　the　definition,　the　higher　Auc　Roc　Score　and　accuracy　are,　the　better　performance　the　model　will　gain.　On　the　modified　version　of　the　PatchCamelyon　(PCam)　benchmark　dataset,　Our　model　achieved　the　highest　AUC　score　(0.971)　and　highest　accuracy　(0.982)　on　the　test　set.