The　irregular　ocean　environment　brings　difficulties　to　observation　and　affects　the　movement　state　of　underwater　vehicles.　Multisource　information　fusion　can　provide　more　reliable　results　on　information-rich　datasets.　This　paper　proposes　a　multisource　information　fusion-based　environment　perception　and　dynamics　model　for　underwater　vehicles　in　irregular　ocean　environments.　First,　the　multisource　observation　data　is　decomposed　by　the　latent　feature　disentanglement　learning　model　into　the　latent　stable　and　dynamical　features.　The　dynamical　feature　is　predicted　by　the　retrospect　regression　method　and　then　integrated　with　the　latent　stable　feature　to　predict　the　ocean　field.　After　that,　the　environment-vehicle　coupling　effect　is　explored　by　thermodynamics　and　statics　simulation.　Then,　a　real-time　dynamic　model　of　the　underwater　vehicle　is　constructed　by　combining　multisource　information　from　physical　principles,　environmental　perception,　and　sensor　observations.　Finally,　extensive　experiments　are　performed　on　a　novel　underwater　glider　and　a　publicly　available　South　China　Sea　dataset　to　verify　the　proposed　method.　©　2023　Elsevier　B.V.