Fuzzy　neural　networks　(FNNs)　have　attracted　considerable　interest　for　modeling　nonlinear　dynamic　systems　in　recent　years.　However,　the　recurrent　design　and　the　self-organizing　design　of　FNNs　generally　lack　adaptability,　and　their　analyses　on　the　change　rule　of　networks　in　continuous　time　are　insufficient.　To　solve　these　problems,　a　self-organizing　recurrent　fuzzy　neural　network　based　on　multivariate　time　series　analysis　(SORFNN-MTSA)　is　proposed　in　this　paper.　First,　a　recurrent　mechanism,　based　on　wavelet　transform　fuzzy　Markov　chain　algorithm,　is　introduced　to　obtain　adaptive　recurrent　values　and　accelerate　convergence　speed　of　the　network.　Second,　a　self-organization　mechanism,　based　on　weighted　dynamic　time　warping　algorithm　and　sensitivity　analysis　algorithm,　is　presented　to　optimize　the　network　structure.　Third,　the　convergence　of　SORFNN-MTSA　is　theoretically　analyzed　to　show　the　efficiency　in　both　fixed　structure　and　self-organizing　structure　cases.　Finally,　several　benchmark　nonlinear　systems　and　a　real　application　of　wastewater　treatment　are　used　to　verify　the　effectiveness　of　SORFNN-MTSA.　Compared　with　other　existing　methods,　the　proposed　SORFNN-MTSA　performs　better　in　terms　of　both　high　accuracy　and　compact　structure.