Sludge　bulking　in　the　municipal　wastewater　treatment　process　will　cause　low　sludge　settling　performance　and　deterioration　of　effluent　quality.　Accurate　identification　and　prediction　of　sludge　bulking　is　an　effective　solution.　Based　upon　the　measured　data,　a　fuzzy　neural　network-based　identification　model　with　self-organizing　recurrent　structure　is　established　in　this　article,　which　can　realize　the　high-precision　identification　of　sludge　bulking.　First,　a　self-organized　method　of　FNN　with　recurrent　structure　is　designed.　The　recurrent　parameters　can　realize　weight　allocation　of　different　time　series.　Second,　the　network　structure　adjustment　index　and　dynamic　structure　adjustment　threshold　are　defined.　A　dynamic　threshold　structure　increment　and　subtraction　method　for　the　self-organized　FNN　is　designed.　The　neuron　rules　and　the　number　of　neurons　are　modified　according　to　the　conditions　of　increasing　and　decreasing,　which　can　determine　the　most　appropriate　neurons　number　and　neuronal　rules.　Then,　the　improved　stochastic　gradient　method　and　the　improved　recursive　least　squares　method　are　employed　to　adjust　network　parameters　to　obtain　accurate　network　output.　Finally,　the　output　accuracy　and　prediction　accuracy　of　the　investigated　model　are　verified　by　simulation　and　comparison　experiments.