Facing　the　structure　design　problem　of　fuzzy　neural　networks　(FNNs),　this　paper　proposed　a　structure　design　approach　based　on　the　recursive　clustering　and　similarity　methods.　First,　a　recursive　clustering　method　to　identify　FNN　structure　was　proposed.　Guided　by　the　strength　of　output　variations　and　using　the　recursive　sub-clustering　as　the　means,　the　proposed　method　determined　the　initial　network　structure　through　recursive　iterations.　Second,　maintaining　a　high　accuracy,　the　method　calculated　the　similarity　degree　between　each　pair　of　fuzzy　rules　and　then　merged　highly　similar　rules　to　simplify　the　initialized　structure　of　the　FNN.　Finally,　numerical　experiments　in　function　approximation　and　nonlinear　system　identification　were　used　to　verify　the　feasibility　and　effectiveness　of　the　proposed　approach.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.