Perineural　invasion　(PNI)　is　the　process　of　neoplastic　invasion　of　the　nerves　and　is　a　prognostic　factor　in　gastric　cancer.　However,　such　examination　is　a　labor-intensive　task　in　high-resolution　digital　pathological　images.　To　alleviate　this　problem,　we　propose　a　multi-task　deep　learning-based　framework　to　highlight　diagnostically　significant　PNI　regions　in　whole　slide　images　(WSIs)　of　human　gastric　cancer　tissue　sections.　The　proposed　framework　includes　a　gastric　cancer　segmentation　model,　neural　detection　model,　and　PNI　decision-making　module,　which　realizes　the　segmentation　of　the　gastric　cancer　region　while　completing　the　task　of　identifying　PNI.　Adequate　comparative　experiments　were　performed　on　our　own　gastric　cancer　PNI　dataset　called　GC-PNI.　Experiments　have　shown　that　our　proposed　model　can　effectively　diagnose　PNI　with　a　high　sensitivity　of　0.972　and　a　specificity　of　0.933,　illustrating　the　potential　of　this　practical　application.