Gastric　cancer　is　a　malignant　neoplasm　with　a　high　mortality　rate　in　the　world.　Nearly　one　million　new　cases　occur　each　year.　The　most　important　measure　to　diagnose　gastric　cancer　is　the　detection　and　treatment　of　diseases　early.　Gastric　cancer　detection　is　currently　performed　by　pathologists　reviewing　large　expanses　of　biological　tissues,　but　this　process　is　labor　intensive　and　error-prone.　In　this　paper,　a　framework　for　automatically　detection　of　tumors　in　gastric　pathology　image　(slide)　has　been　proposed　based　on　deep　learning.　A　deep　residual　network　with　50　layers　is　built　by　identity　mapping　on　a　dataset　of　pathology　images.　The　proposed　method　makes　the　training　of　models　easier　and　improves　the　generalization　performance.　Finally,　the　experimental　results　show　that　the　F-score　of　our　method　achieves　96%.　The　research　in　auto-classification　of　gastric　pathology　images　has　great　value　for　gastric　cancer　detection　in　clinical　medicine.