Improving　the　performance　of　machine　learning　algorithms　to　overcome　the　curse　of　dimensionality　while　maintaining　interpretability　is　still　a　challenging　issue　for　researchers　in　artificial　intelligence.　Patch　learning　(PL),　based　on　the　improved　adaptive　network-based　fuzzy　inference　system　(ANFIS)　and　continuous　local　optimization　for　the　input　domain,　is　characterized　by　high　accuracy.　However,　PL　can　only　handle　low-dimensional　data　set　regression.　Based　on　the　parallel　and　serial　ensembles,　two　deep　patch　learning　algorithms　with　embedded　adaptive　fuzzy　systems　(DPLFSs)　are　proposed　in　this　paper.　First,　using　the　maximum　information　coefficient　(MIC)　and　Pearson＇s　correlation　coefficients　for　feature　selection,　the　variables　with　the　least　relationship　(linear　or　nonlinear)　are　excluded.　Second,　principal　component　analysis　is　used　to　reduce　the　complexity　further　of　DPLFSs.　Meanwhile,　fuzzy　C-means　clustering　is　used　to　enhance　the　interpretability　of　DPLFSs.　Then,　an　improved　PL　method　is　put　forward　for　the　training　of　each　sub-fuzzy　system　in　a　fashion　of　bottom-up　layer-by-layer,　and　finally,　the　structure　optimization　is　performed　to　significantly　improve　the　interpretability　of　DPLFSs.　Experiments　on　several　benchmark　data　sets　show　the　advantages　of　a　DPLFS:　(1)　it　can　handle　medium-scale　data　sets;　(2)　it　can　overcome　the　curse　of　dimensionality　faced　by　PL;　(3)　its　precision　and　generalization　are　greatly　improved;　and　(4)　it　can　overcome　the　poor　interpretability　of　deep　learning　networks.　Compared　with　shallow　and　deep　learning　algorithms,　DPLFSs　have　the　advantages　of　interpretability,　self-learning,　and　high　precision.　DPLFS1　is　superior　for　medium-scale　data;　DPLFS2　is　more　efficient　and　effective　for　high-dimensional　problems,　has　a　faster　convergence,　and　is　more　interpretable.