While 3D Gaussian Splatting (3DGS) has excelled in dynamic scene reconstruction, it struggles with multi-view object motion blur, where view-dependent non-uniform blur violates fundamental multi-view geometric constraints. Existing methods fail to balance complex motion fitting with physical consistency across different views. To address the challenge, we propose TaylorMoDe-GS, the first 3DGS framework tailored for multi-view dynamic object deblurring. Specifically, we shift the modeling paradigm from displacement fitting to velocity driven modeling. This is achieved by analytically deriving instantaneous 3D velocities via Taylor series. To map 3D physical motion onto the 2D image plane, we propose a velocity splatting technique. Building upon this, we introduce a neural Peano remainder network to compensate for high frequency non-linear dynamics, effectively resolving the conflict between physical priors and fitting flexibility. Combined with a learnable physical blur synthesis mechanism, our framework ensures rigorous spatiotemporal and view consistency. Extensive experimental results validate that our method achieves notable advancements in restoring high-fidelity dynamic scenes and physically consistent motion fields, effectively addressing the challenges of multi-view dynamic object deblurring. The code is released at https://github.com/Chiffin-0816/TaylorMoDe-GS.