This　paper　proposes　a　novel　mean-field　matrix-analytic　method　in　the　study　of　bike　sharing　systems,　in　which　a　Markovian　environment　is　constructed　to　express　time-inhomogeneity　and　asymmetry　of　processes　that　customers　rent　and　return　bikes.　To　achieve　effective　computability　of　this　mean-field　method,　this　study　provides　a　unified　framework　through　the　following　three　basic　steps.　The　first　one　is　to　deal　with　a　major　challenge　encountered　in　setting　up　mean-field　block-structured　equations　in　general　bike　sharing　systems.　Accordingly,　we　provide　an　effective　technique　to　establish　a　necessary　reference　system,　which　is　a　time-inhomogeneous　queue　with　block　structures.　The　second　one　is　to　prove　asymptotic　independence　(or　propagation　of　chaos)　in　terms　of　martingale　limits.　Note　that　asymptotic　independence　ensures　and　supports　that　we　can　construct　a　nonlinear　quasi-birth-and-death　(QBD)　process,　such　that　the　stationary　probability　of　problematic　stations　can　be　computed　under　a　unified　nonlinear　QBD　framework.　Lastly,　in　the　third　step,　we　use　some　numerical　examples　to　show　the　effectiveness　and　computability　of　the　mean-field　matrix-analytic　method,　and　also　to　provide　valuable　observation　of　the　influence　of　some　key　parameters　on　system　performance.　We　are　optimistic　that　the　methodology　and　results　given　in　this　paper　are　applicable　in　the　study　of　general　large-scale　bike　sharing　systems.