Grid　partitioning　for　input　space　results　in　the　exponential　rise　in　the　number　of　rules　in　adaptive　network-based　fuzzy　inference　system　(ANFIS)　and　patch　learning　(PL)　as　the　number　of　features　increases,　thus　resulting　in　the　huge　computational　load　and　deteriorating　its　interpretability.　An　improved　PL　(iPL)　is　put　forward　for　the　training　of　each　sub-fuzzy　system　to　overcome　the　rule-explosion　problem.　In　the　iPL,　input　partitioning　is　done　using　fuzzy　c-means　(FCM)　clustering　to　avoid　the　heavy　computational　complexity　arising　due　to　the　large　number　of　rules　generated　from　high　dimensionality.　In　this　paper,　two　novel　classifiers,　called　FCM　clustering　based　deep　patch　learning　with　improved　high-level　interpretability　for　classification　problems,　are　presented,　named　as　HI-FCMDPL-CP1　and　HI-FCMDPL-CP2.　The　proposed　classifiers　have　two　characteristics:　One　is　a　stacked　deep　structure　of　component　iPL　fuzzy　classifiers　for　high　accuracy,　and　the　other　is　the　use　of　maximal　information　coefficient　(MIC)　and　the　maximum　misclassification　threshold　(MMT)　to　optimize　the　deep　structures.　High　interpretability　is　achieved　at　each　layer　by　using　the　FCM　clustering,　concise　structure　and　large　input　dimensionality.　The　MMT,　random　input　(RI)　and　parameter　sharing　(PS)　are　integrated　to　improve　their　classification　accuracy　without　losing　their　interpretability.　Experiments　on　several　real-word　datasets　demonstrated　that　MIC,　RI　and　PS　in　HI-FCMDPL-CP1　and　HI-FCMDPL-CP2　are　effective　individually,　and　integrating　them　all　three　can　further　improve　the　classification　performance.　A　more　concise　deep　fuzzy　system　is　obtained　with　the　number　of　features　and　fuzzy　rules　reduced　simultaneously.　Furthermore,　MIC,　RI　and　PS　are　used　to　determine　the　advantages　and　disadvantages　of　using　serial　versus　parallel　structures　to　avoid　subjective　selection　of　these　two　categories.