Interval　type-2　fuzzy　neural　networks　(IT2FNNs)　have　been　widely　used　for　modeling　in　industrial　processes,　and　the　efficient　parameter　learning　methods　are　crucial　for　obtaining　accurate　models.　However,　the　problem　of　low　computational　efficiency　and　poor　convergence　performance　still　exists　in　existing　learning　methods.　In　this　study,　a　novel　hierarchical　learning　algorithm　is　proposed　for　constructing　IT2FNN　with　better　learning　ability　and　generalization　performance.　Firstly,　the　improved　recursive　least　square　(RLS)　method　with　an　adaptive　forgetting　factor　is　designed　to　learn　the　consequent　parameters.　Next,　the　advanced　stochastic　gradient　descent　(SGD)　method　is　adopted　to　learn　the　antecedent　parameters.　Finally,　the　proposed　method　is　applied　to　real　industrial　processes　and　compared　with　various　state-of-the-art　IT2FNNs.　Simulation　results　demonstrate　the　superiority　of　the　proposed　method.　©　2023　IEEE.