Type-2　fuzzy　neural　networks　(T2FNNs)　have　gained　popularity　due　to　their　processing　ability　for　high　uncertainty.　However,　concerned　with　the　high-dimensional　problems　of　nonlinear　systems,　the　interpretability　of　individual　T2FNNs　is　weak　due　to　the　exponential　growth　of　fuzzy　rules.　To　deal　with　this　problem,　an　information　orientation-based　modular　T2FNN　(IO-MT2FNN)　is　developed　to　improve　its　interpretability　in　this　paper.　First,　an　information　entropy-based　decomposition　method　is　designed　to　divide　the　original　input　space　into　three　sub-spaces,　namely　edge,　local　and　global　regions.　Then,　the　information　with　different　attributes　is　separated　to　provide　an　unambiguous　interpretation.　Second,　the　independent　module　describing　these　regions　with　type-2　fuzzy　sets　is　embedded　in　the　membership　function　layer　of　IO-MT2FNN　to　represent　the　coupling　relationship　between　regional　information　in　an　interpretable　way.　Third,　an　information　mapping　strategy　is　introduced　with　low-order　Gaussian　kernel　matrices,　instead　of　a　high-order　mapping　matrix,　to　extract　the　features　from　the　allocated　information　in　each　module,　which　enables　IO-MT2FNN　to　achieve　a　compact　topology　through　dimensionality　reduction.　Finally,　the　simulations　demonstrate　that　the　proposed　IO-MT2FNN　can　compete　with　the　advanced　approaches　in　terms　of　interpretability　for　the　prediction　of　high-dimensional　and　complex　systems.　©　2024　Elsevier　Inc.