Interval　type-2　fuzzy　neural　network　(IT2FNN)　has　extensive　applications　for　modeling　nonlinear　systems　with　multidimensional　structured　data.　However,　the　traditional　IT2FNN　based　on　the　structured　topology　struggles　to　identify　nonlinear　systems　using　semistructured　and　unstructured　data.　To　tackle　this　issue,　a　multimodal　learning-based　IT2FNN　(ML-IT2FNN)　is　developed　for　joint　learning　of　the　multimodal　data.　First,　an　encoding　layer　with　a　multimodal　perception　strategy　is　designed　to　identify　the　multimodal　information.　The　parameterized　modalities　are　utilized　to　map　the　features　of　the　semistructured　and　unstructured　data　into　the　structured　spaces.　Second,　a　multimodal　representation　mechanism　is　introduced　to　extract　the　features　of　multiple　modalities　from　the　structured　spaces.　In　this　mechanism,　type-2　fuzzy　sets　with　soft　boundaries　are　used　to　intricate　coupling　relationships　among　modalities　by　adapting　to　the　nuances　of　multimodal　data.　Third,　a　constrained　hybrid　learning　algorithm,　combining　parallel　and　sequential　updating　frameworks,　is　presented　to　optimize　the　parameters　of　ML-IT2FNN.　The　type-2　fuzzy　parameters　and　the　coupling　parameters　with　constraints　are　updated　adaptively　to　facilitate　the　intramodal　identification　performance　and　cross-modal　interaction　performance.　Finally,　a　series　of　examples　in　nonlinear　systems　are　introduced　to　verify　ML-IT2FNN.　Empirical　results　demonstrate　that　ML-IT2FNN　surpasses　the　cutting-edge　approaches　with　accuracy.