Fuzzy　neural　networks　(FNNs)　hold　the　advantages　of　knowledge　leveraging　and　adaptive　learning,　which　have　been　widely　used　in　nonlinear　system　modeling.　However,　it　is　difficult　for　FNNs　to　obtain　the　appropriate　structure　in　the　situation　of　insufficient　data,　which　limits　its　generalization　performance.　To　solve　this　problem,　a　data-knowledge-driven　self-organizing　FNN　(DK-SOFNN)　with　a　structure　compensation　strategy　and　a　parameter　reinforcement　mechanism　is　proposed　in　this　article.　First,　a　structure　compensation　strategy　is　proposed　to　mine　structural　information　from　empirical　knowledge　to　learn　the　structure　of　DK-SOFNN.　Then,　a　complete　model　structure　can　be　acquired　by　sufficient　structural　information.　Second,　a　parameter　reinforcement　mechanism　is　developed　to　determine　the　parameter　evolution　direction　of　DK-SOFNN　that　is　most　suitable　for　the　current　model　structure.　Then,　a　robust　model　can　be　obtained　by　the　interaction　between　parameters　and　dynamic　structure.　Finally,　the　proposed　DK-SOFNN　is　theoretically　analyzed　on　the　fixed　structure　case　and　dynamic　structure　case.　Then,　the　convergence　conditions　can　be　obtained　to　guide　practical　applications.　The　merits　of　DK-SOFNN　are　demonstrated　by　some　benchmark　problems　and　industrial　applications.