As　a　model　for　reasoning　and　decision-making　based　on　fuzzy　rules,　fuzzy　systems　have　high　interpretability.　However,　when　the　data　dimension　increases,　the　fuzzy　system　will　face　the　problem　of　＂rule　explosion＂,　making　it　difficult　to　learn　and　predict　effectively.　In　this　paper,　the　fuzzy　system　trained　by　the　FLOWFS　(Fast-Learning　with　OptimalWeights　for　Fuzzy　Systems)　algorithm　is　used　as　sub-module　in　the　deep　fuzzy　system,　and　the　deep　fuzzy　system　DFLOWFS　(Deep　FLOWFS)　is　constructed　from　the　bottom-up　hierarchical　structure　as　the　following　three　steps.　1)　The　FLOWFS　algorithm　assigns　weight　attributions　to　each　fuzzy　rule,　and　the　rule　weights　are　trained　by　the　least　square　method　with　regularization　terms　to　shorten　training　time　and　improve　accuracy.　2)　Three　strategies　of　dividing　high-dimensional　inputs　into　multiple　low-dimensional　inputs　are　proposed　as　sequential　division,　random　division　and　correlation　division.　Then,　it　is　verified　by　experiments　that　the　correlation　division　has　the　best　performance.　3)　The　sub-module　discarding　method　is　proposed　to　discard　the　sub-modules　with　poor　performance　to　have　a　maximum　improvement　of　13.8%　compared　to　the　DFLOWFS　without　using　the　sub-module　discarding　method.　Then,　the　optimized　DFLOWFS　is　verified　and　compared　with　the　other　three　classic　regression　models　on　the　three　UCI　datasets.　Experiments　show　that　with　the　increase　of　the　data　dimension,　DFLOWFS　not　only　have　good　interpretability　but　also　have　good　accuracy.　Furthermore,　DFLOWFS　performs　best　among　all　models　in　comprehensive　scores,　with　good　learning　ability　and　generalization　ability.　Therefore,　the　proposed　strategies　with　hierarchical　structure　for　optimal　shallowfuzzy　systems　are　effective,　which　give　a　newinsight　for　fuzzy　system　research.