Interval　type-2　fuzzy　neural　networks　(IT2FNNs)　usually　stack　adequate　fuzzy　rules　to　identify　nonlinear　systems　with　high-dimensional　inputs,　which　may　result　in　an　explosion　of　fuzzy　rules.　To　cope　with　this　problem,　a　self-organizing　IT2FNN,　based　on　the　information　aggregation　method　(IA-SOIT2FNN),　is　developed　to　avoid　the　explosion　of　fuzzy　rules　in　this　article.　First,　a　relation-aware　strategy　is　proposed　to　construct　rotatable　type-2　fuzzy　rules　(RT2FRs).　This　strategy　uses　the　individual　RT2FR,　instead　of　multiple　standard　fuzzy　rules,　to　interpret　interactive　features　of　high-dimensional　inputs.　Second,　a　comprehensive　information　evaluation　mechanism,　associated　with　the　interval　information　and　rotation　information　of　RT2FR,　is　developed　to　direct　the　structural　adjustment　of　IA-SOIT2FNN.　This　mechanism　can　achieve　a　compact　structure　of　IA-SOIT2FNN　by　growing　and　pruning　RT2FRs.　Third,　a　multicriteria-based　optimization　algorithm　is　designed　to　optimize　the　parameters　of　IA-SOIT2FNN.　The　algorithm　can　simultaneously　update　the　rotatable　parameters　and　the　conventional　parameters　of　RT2FR,　and　further　maintain　the　accuracy　of　IA-SOIT2FNN.　Finally,　the　experiments　showcase　that　the　proposed　IA-SOIT2FNN　can　compete　with　the　state-of-the-art　approaches　in　terms　of　identification　performance.