Ensuring　the　stability　of　heavy　multi-axle　vehicles　necessitates　the　accurate　calibration　and　decoupling　of　Multiaxis　Wheel　Force　Sensors　(MWFS).　Traditional　methods　often　neglect　the　temporal　coupling　present　in　MWFS　output　data,　leading　to　reduced　accuracy.　This　paper　introduces　an　Improved　Decoupling　Algorithm　(IDI)　based　on　the　Informer　network,　designed　to　temporally　decouple　MWFS　and　enhance　precision.　The　Decoupling　embedding　layer　(DE)　performs　linear　decoupling　of　the　MWFS,　while　the　Token　embedding　layer　(TE)　and　Informer　encoder　extract　timing　coupling　features.　The　highway　network　and　linear　fully-connected　layer　then　provide　nonlinear　decoupling　compensation.　Experimental　results　demonstrate　that　the　IDI　algorithm　significantly　outperforms　traditional　methods　like　Extreme　Learning　Machine　(ELM)　and　Back　Propagation　Network　(BPNN),　achieving　at　least　a　41.12%　improvement　in　accuracy　in　the　highly　coupled　Mz　channel.　In　conclusion,　the　IDI　algorithm　not　only　achieves　high-precision　decoupling　of　MWFS　but　also　presents　a　robust　framework　for　modeling　various　sensor　types.