Long　time　series　and　accurate　subsurface　temperature　data　in　the　global　ocean　are　essential　for　ocean　warming　and　climate　change　studies.　The　sparse　in　situ　observations　in　the　pre-Argo　era　hinder　the　reconstruction　of　long-time　series　observational　data　for　the　global　ocean.　This　study　proposes　a　novel　Adaptive　Spatio-TEmporal　Neighbors　with　two-point　differences　(ASTEN)　method　for　subsurface　temperature　reconstruction,　which　adaptively　learns　and　adjusts　spatio-temporal　neighbors　depending　on　the　distribution　of　in　situ　observations　to　ensure　robust　gaps-filling　performance　across　four　dimensions.　By　integrating　geoscience　domain　knowledge　and　utilizing　spatiotemporal　autocorrelation,　ASTEN　simultaneously　learns　the　spatial　pattern　and　temporal　variation　of　subsurface　temperature,　and　significantly　enhances　the　interpretability　and　accuracy　of　ocean　temperature　reconstructions　over　a　long　time　series　compared　to　the　DINCAE　and　DINEOF.　The　ASTEN　reconstructed　temperature　data　for　the　upper　1000　m　from　1960　to　2022　can　effectively　track　the　ocean　warming　process　for　more　than　six　decades.　This　study　demonstrates　the　ASTEN　method　is　well　suited　for　subsurface　temperature　reconstruction,　and　holds　great　potential　in　the　gaps-filling　of　sparse　ocean　observations　with　high　missing　rates　over　a　large　scale.　The　new　reconstruction　of　subsurface　temperature　can　effectively　reduce　the　uncertainty　of　subsurface　ocean　warming　analysis.