Much　like　genomics,　brain　connectomics　has　rapidly　become　a　core　component　of　most　national　brain　projects　around　the　world.　Beyond　the　ambitious　aims　of　these　projects,　a　fundamental　challenge　is　the　need　for　an　efficient,　robust,　reliable　and　easy-to-use　pipeline　to　mine　such　large　neuroscience　datasets.　Here,　we　introduce　a　computational　pipeline-namely　the　Connectome　Computation　System　(CCS)-for　discovery　science　of　human　brain　connectomes　at　the　macroscale　with　multimodal　magnetic　resonance　imaging　technologies.　The　CCS　is　designed　with　a　three-level　hierarchical　structure　that　includes　data　cleaning　and　preprocessing,　individual　connectome　mapping　and　connectome　mining,　and　knowledge　discovery.　Several　functional　modules　are　embedded　into　this　hierarchy　to　implement　quality　control　procedures,　reliability　analysis　and　connectome　visualization.　We　demonstrate　the　utility　of　the　CCS　based　upon　a　publicly　available　dataset,　the　NKI-Rockland　Sample,　to　delineate　the　normative　trajectories　of　well-known　large-scale　neural　networks　across　the　natural　life　span　(6-85　years　of　age).　The　CCS　has　been　made　freely　available　to　the　public　via　GitHub　(https://github.com/zuoxinian/CCS)　and　our　laboratory＇s　Web　site　(http://lfcd.psych.ac.cn/ccs.html)　to　facilitate　progress　in　discovery　science　in　the　field　of　human　brain　connectomics.