The　convergence　of　systems　neuroscience　and　open　science　arouses　great　interest　in　the　current　brain　big　data　era,　highlighting　the　thinking　capability　of　intelligent　agents　in　handling　multi-source　knowledge,　information　and　data　across　various　levels　of　granularity.　To　realize　such　thinking-inspired　brain　computing　during　a　brain　investigation　process,　one　of　the　major　challenges　is　to　find　a　holistic　brain　map　that　can　model　multi-dimensional　variables　of　brain　investigations　across　brain　functions,　experimental　tasks,　brain　data　and　analytical　methods　synthetically.　In　this　paper,　we　propose　a　context-enhanced　graph　learning　method　to　fuse　open　knowledge　from　different　sources,　including:　contextual　information　enrichment,　structural　knowledge　fusion,　and　holistic　graph　learning.　Such　a　method　can　enhance　contextual　learning　of　abstract　concepts　and　relational　learning　between　two　concepts　that　have　large　gap　from　different　dimensions.　As　a　result,　an　extensible　space,　namely　Thinking　Space,　is　generated　to　represent　holistic　variables　and　their　relations　in　a　map,　which　currently　contributes　to　the　field　of　brain　research　for　systematic　brain　computing.　In　the　future,　the　Thinking　Space　coupled　with　the　rapid　development　and　spread　of　artificial　intelligence　generated　content　will　be　developed　in　more　scenarios　so　as　to　promote　global　interactions　of　intelligence　in　the　connected　world.　　©　2023　-　IOS　Press.　All　rights　reserved.