Many　nonlinear　dynamical　systems　are　usually　lack　of　abundant　datasets　since　the　data　acquiring　process　is　time　consuming.　It　is　difficult　to　utilize　the　incomplete　datasets　to　build　an　effective　data-driven　model　to　improve　the　industry　productivity.　To　overcome　this　problem,　a　data-knowledge-based　fuzzy　neural　network　(DK-FNN)　was　developed　in　this　article.　Compared　with　the　existing　methods,　the　proposed　DK-FNN　consists　of　the　following　obvious　advantages.　First,　through　the　multilayered　connectionist　structure,　this　proposed　DK-FNN　could　not　only　make　full　use　of　the　data　from　the　current　scene,　but　also　use　the　existing　knowledge　from　the　source　scene　to　improve　the　learning　performance.　Second,　an　integrated-form　transfer　learning　(ITL)　method　was　developed　to　improve　the　learning　performance　of　DK-FNN.　This　first　reported　ITL　method　was　able　to　integrate　the　internal　information　from　the　datasets　in　the　source　scene　and　the　knowledge　from　the　current　scene　to　offset　the　data　shortage　in　the　learning　process.　Third,　a　mutual　attraction　strategy　(MAS)　was　designed　to　balance　the　difference　of　data　distributions　to　reduce　the　identification　errors　of　DK-FNN.　Then,　the　proposed　DK-FNN　was　able　to　satisfy　the　nonlinear　dynamical　systems.　Finally,　the　effectiveness　and　the　merit　of　DK-FNN　were　validated　by　applying　it　to　several　practical　systems.