Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly from hematoxylin-eosin (HE) staining whole-slide images (WSIs). PDA, poorly differentiated adenocarcinoma. WDA, well-differentiated adenocarcinoma.