Aiming　at　the　problem　that　the　fuzzy　neural　network　structure　is　difficult　to　adapt　when　there　is　no　growth　and　pruning　thresholds,　this　paper　proposes　a　structure　design　method　based　on　hybrid　evaluation　index　(HEI).　First,　the　initial　number,　centers　and　widths　of　rule　neurons　are　determined　by　the　fuzzy　C-means　clustering　algorithm.　Next,　a　novel　relevance　evaluation　index　(REI),　which　is　composed　of　the　Davies　bouldin　index　(DBI)　and　the　Dunn　index　(DI),　is　presented　to　calculate　the　correlation　among　the　outputs　of　rule　neurons.　The　learning　ability　of　neural　network　will　be　determined　by　the　change　of　root　mean　square　error　(RMSE)　during　the　training　process.　Then,　the　HEI　is　presented　based　on　REI　and　RMSE.　The　topology　structure　of　the　fuzzy　neural　network　is　adjusted　according　to　the　HEI.　Finally,　the　feasibility　and　effectiveness　of　the　structure　design　method　are　proved　by　using　the　Mackey-Glass　time　series　prediction,　nonlinear　system　identification　and　PM2.5　concentration　prediction.　©　All　Right　Reserved.