Many　representation　learning　methods　have　gradually　emerged　to　better　exploit　the　properties　of　multi-view　data.　However,　these　existing　methods　still　have　the　following　areas　to　be　improved:　1)　Most　of　them　overlook　the　ex-ante　interpretability　of　the　model,　which　renders　the　model　more　complex　and　more　difficult　for　people　to　understand;　2)　They　underutilize　the　potential　of　the　bi-topological　spaces,　which　bring　additional　structural　information　to　the　representation　learning　process.　This　lack　is　detrimental　when　dealing　with　data　that　exhibits　topological　properties　or　has　complex　geometrical　relationships　between　different　views.　Therefore,　to　address　the　above　challenges,　we　propose　an　optimization-oriented　multi-view　representation　learning　framework　in　implicit　bi-topological　spaces.　On　one　hand,　we　construct　an　intrinsically　interpretability　end-to-end　white-box　model　that　directly　conducts　the　representation　learning　procedure　while　improving　the　transparency　of　the　model.　On　the　other　hand,　the　integration　of　bi-topological　spaces　information　within　the　network　via　manifold　learning　facilitates　the　comprehensive　utilization　of　information　from　the　data,　ultimately　enhancing　representation　learning　and　yielding　superior　performance　for　downstream　tasks.　Extensive　experimental　results　demonstrate　that　the　proposed　method　exhibits　promising　performance　and　is　feasible　in　the　downstream　tasks.