Multi-Agent　Path　Finding　(MAPF)　aims　to　plan　conflict-free　joint　optimal　paths　effectively　for　a　group　of　agents　from　their　respective　starting　points　to　destinations.　This　task　is　challenging　due　to　its　computational　complexity.　Current　works　focus　on　reducing　the　number　of　potential　conflicts　to　further　decrease　the　time　required　for　path　planning.　In　this　study,　we　integrate　deep　learning-based　architecture　with　the　sub-dimension　expansion　method,　aiming　to　transform　the　multi-agent　pathfinding　task　into　an　image　classification　task.　We　employ　visual　models　as　single-agent　path　planner　to　predict　optimal　path　based　on　the　observation　space　of　each　agent,　which　reduces　the　number　of　conflicts　and　improves　the　performance　of　the　planner　significantly.　The　experimental　results　show　that,　in　comparison　to　Learning-Assisted　M∗,　our　method　achieves　a　reduction　in　potential　conflicts　and　implements　MAPF　task　effectively　only　with　a　minor　increase　in　time　consumption.　©　2024　Asian　Control　Association.