Tunnel　engineering　is　one　of　the　hot　spots　of　research　in　the　field　of　geotechnical　engineering,　and　the　seepage　analysis　of　tunnels　is　an　important　research　direction　at　present.　In　recent　years,　physics-informed　deep　learning　based　on　priori　fusion　data　has　become　a　cross-disciplinary　hotspot　for　solving　forward　and　inverse　problems　based　on　partial　differential　equations　(PDEs).　In　this　paper,　physics-informed　deep　learning　(PIDL)　is　introduced　to　the　solution　of　PDEs　for　Geotechnical　Engineering　problems.　This　paper　builds　relevant　theoretical　models　and　systematically　discusses　the　issues　associated　with　applying　this　method　to　the　numerical　simulation　of　tunnel　seepage,　starting　from　the　mathematical　theory　of　physics-informed　deep　learning.　The　results　of　this　paper　are　compared　with　the　analytical　solution　and　the　finite　element　method,　and　the　generalization　accuracy　of　the　neural　network　is　tested　by　replacing　different　boundary　conditions,　which　verifies　the　feasibility　of　the　physicsinformed　deep　learning　method　for　solving　the　seepage　problem　of　tunnels　with　nonhomogeneous　porous　media.　The　results　of　several　typical　numerical　examples　show　that　the　method　has　the　advantages　of　meshless　and　refined　simulation.