When　a　firefighting　incident　occurs　in　a　wild　complex　mountain　with　no　obvious　roads　or　sparse　roads,　it　is　crucial　to　plan　a　safe　and　fast　route　through　the　complex　mountain　environment.　Aiming　at　the　problem　that　Ant　Colony　Optimization　(ACO)　is　easy　to　fall　into　local　optimum　and　the　search　time　is　long　for　complex　mountain　path　planning,　our　study　proposes　an　ACO　algorithm　for　hiking　emergency　rescue　path　planning,　which　is　suitable　for　fine-grained　wild　mountain　environments.　Firstly,　our　study　analyzed　the　relationship　between　surface　information　and　human　movement　speed　based　on　existing　literature　and　designed　the　objective　function　and　heuristic　function　of　the　optimization　algorithm　considering　two　factors:　surface　shrub　cover　and　terrain　slope.　Then,　we　used　a　combination　of　plane　and　field　of　view　ant　search　combined　with　heuristic　function　and　pheromone　concentration　to　determine　the　next　grid　to　be　selected　in　the　optimization　process　of　the　improved　algorithm.　Finally,　the　improved　algorithm　used　a　Laplace　distribution　to　adjust　the　initial　pheromone　to　improve　the　quality　of　the　algorithm＇s　initial　solution.　For　the　deadlock　problem,　the　improved　algorithm　added　isolated　pheromones　to　prevent　the　next　ant　from　falling　into　a　deadlock　dilemma.　The　improved　algorithm　used　a　genetic　operator　with　grouping　to　update　the　global　regular　pheromone　to　avoid　the　ant　colony　from　falling　into　a　local　optimum　dilemma.　In　our　study,　we　applied　four　ACO　to　the　wild　mountain　environment　of　400×400　grids,　1000　grids×1000　grids,　5000　grids×5000　grids,　and　10　000　grids×10　000　grids　for　comparison,　and　set　different　starting　and　ending　points　for　each　environment.　The　experimental　results　show　that　each　ACO　using　a　combined　planar　and　visual　field　search　approach　can　obtain　feasible　paths　in　all　four　experiments,　which　verified　the　feasibility　of　the　method.　The　quality　of　the　paths　using　the　improved　algorithms　was　better　than　the　other　three　algorithms,　with　improvements　of　0.52%~4.95%,　4.71%~5.39%,　2.26%~13.11%,　and　3.84%~9.16%　in　the　four　experiments,　respectively,　and　the　improved　algorithm　had　shorter　search　time　and　convergence　time.　In　addition,　the　combined　planar　and　visual　field　search　approach　reduced　the　search　space　and　improved　the　computational　efficiency　of　the　algorithm　in　the　field　3D　mountain　environment.　This　search　method　was　faster　than　the　8-connected　method　and　reduced　the　average　time　consumption　by　more　than　90%.　Our　algorithm　is　suitable　for　hiking　path　planning　research　in　large　3D　mountain　scenes,　with　reduced　planning　time　and　improved　path　quality,　providing　technical　support　for　the　work　of　finding　the　best　3D　mountain　hiking　paths　without　road　networks.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.