The　classification　of　grasping　states　is　crucial　for　robots＇　stable　operations　that　based　on　its　real-timely　results,　the　robot　can　adjust　the　appropriate　gripping　force.　However,　until　now,　classifying　stable　grasping　states　of　multiple　objects　in　different　scenarios　is　still　a　challenging　issue　since　it　needs　to　find　the　commonality　of　different　objects　in　the　same　state.　Aiming　to　solve　this　problem,　we　present　an　effective　stable　grasping　states　classification　method　based　on　the　combination　of　wavelet　fuzzy　entropy　and　t-Distributed　Stochastic　Neighbor　Embedding　(t-SNE),　and　deploys　it　to　the　robot　platform　to　complete　the　state　feedback.　Firstly,　the　wavelet　decomposition　of　the　tactile　sensor　signal　is　performed　and　the　fuzzy　entropy　of　the　signal　is　calculated,　and　the　time-domain　feature　variables　are　extracted　together　to　form　the　eigenvector　set.　Secondly,　t-SNE　is　used　to　reduce　the　dimensionality　and　visualize　the　high-dimensional　data.　Finally,　the　extracted　features　are　input　into　Support　Vector　Machine　(SVM)　to　classify　the　stable　state　of　the　t-SNE　data　after　dimensionality　reduction.　The　experimental　results　show　that　the　proposed　method　has　strong　stability　recognition　ability,　the　accuracy　rate　reaches　90.37%,　the　average　success　rate　of　grasping　state　feedback　reaches　91.1%,　and　the　average　success　rate　is　increased　by　5.53%　compared　with　the　Principal　Component　Analysis　(PCA)　feature　processing　method.　　©　2024　IEEE.