Transfer　learning　algorithm　can　provide　a　framework　to　utilize　the　previous　knowledge　to　train　fuzzy　neural　network　(FNN).　However,　the　performance　of　TL-based　FNN　will　be　destroyed　by　the　knowledge　over-fitting　problem　in　the　learning　process.　To　solve　this　problem,　an　interactive　transfer　learning　(ITL)　algorithm,　which　can　alleviate　the　negative　transfer　among　different　domains　to　improve　the　learning　performance　of　FNN,　is　designed　and　analyzed　in　this　article.　This　ITL-assisted　FNN　(ITL-FNN)　contains　the　following　advantages.　First,　a　knowledge　filter　algorithm　is　developed　to　reconstruct　the　knowledge　in　source　scene　by　balancing　the　matching　accuracy　and　diversity.　Then,　the　knowledge　from　source　scene　can　fit　the　instance　of　target　scene　with　suitable　accuracy.　Second,　a　self-balancing　mechanism　is　designed　to　balance　the　driven　information　between　the　source　and　target　scenes.　Then,　the　knowledge　can　be　refitted　to　reduce　the　useless　information.　Third,　a　structural　competition　algorithm　is　proposed　to　adjust　the　knowledge　of　FNN.　Then,　the　proposed　ITL-FNN　can　achieve　compact　structure　to　improve　the　generalization　performance.　Finally,　some　benchmark　problems　and　industrial　applications　are　provided　to　demonstrate　the　merits　of　ITL-FNN.