The　graph-information-based　fuzzy　clustering　has　shown　promising　results　in　various　datasets.　However,　its　performance　is　hindered　when　dealing　with　high-dimensional　data　due　to　challenges　related　to　redundant　information　and　sensitivity　to　the　similarity　matrix　design.　To　address　these　limitations,　this　article　proposes　an　implicit　fuzzy　k-means　(FKMs)　model　that　enhances　graph-based　fuzzy　clustering　for　high-dimensional　data.　Instead　of　explicitly　designing　a　similarity　matrix,　our　approach　leverages　the　fuzzy　partition　result　obtained　from　the　implicit　FKMs　model　to　generate　an　effective　similarity　matrix.　We　employ　a　projection-based　technique　to　handle　redundant　information,　eliminating　the　need　for　specific　feature　extraction　methods.　By　formulating　the　fuzzy　clustering　model　solely　based　on　the　similarity　matrix　derived　from　the　membership　matrix,　we　mitigate　issues,　such　as　dependence　on　initial　values　and　random　fluctuations　in　clustering　results.　This　innovative　approach　significantly　improves　the　competitiveness　of　graph-enhanced　fuzzy　clustering　for　high-dimensional　data.　We　present　an　efficient　iterative　optimization　algorithm　for　our　model　and　demonstrate　its　effectiveness　through　theoretical　analysis　and　experimental　comparisons　with　other　state-of-the-art　methods,　showcasing　its　superior　performance.