As　an　essential　component　of　urban　infrastructure　construction,　polyethylene　(PE)　pipelines　face　the　challenging　task　of　underground　detection　due　to　the　complex　and　dynamic　nature　of　the　subsurface　environment,　diverse　installation　paths,　and　the　inherent　insulating　properties　of　PE　materials.　In　order　to　address　the　non-excavation　detection　of　buried　PE　pipelines,　this　paper　proposes　an　acoustic　method　based　on　the　long　short-term　memory　(LSTM)　neural　network.　The　study　begins　by　analyzing　the　propagation　and　reflection　mechanisms　of　elastic　waves　in　the　pipe-soil　coupling　system,　and　a　impact　excitation　source　is　designed　to　generate　the　excitation　signal.　After　establishing　the　experimental　environment　and　collecting　experimental　data,　a　comprehensive　analysis　is　conducted,　and　the　LSTM　neural　network　is　employed　for　data　classification　to　determine　the　presence　of　buried　PE　pipelines.　Through　neural　network　training,　accurate　identification　of　the　PE　pipeline＇s　existence　and　prediction　of　its　burial　depth　are　achieved,　providing　an　efficient　and　reliable　solution　for　buried　PE　pipeline　detection.　The　practical　results　demonstrate　the　significant　application　prospects　of　the　combined　acoustic　method　and　LSTM　neural　network　in　buried　PE　pipeline　detection.　This　research　contributes　a　novel　solution　to　the　field　of　non-destructive　PE　pipeline　detection,　with　both　theoretical　and　practical　implications.