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Despite its prevalence in experiments, the influence of complex strain on material properties remains understudied due to the lack of effective simulation methods. Here, the effects of bending, rippling, and bubbling on the ferroelectric domains are investigated in an In 2 Se 3 monolayer by density functional theory and deep learning molecular dynamics simulations. Since the ferroelectric switching barrier can be increased (decreased) by tensile (compressive) strain, automatic polarization reversal occurs in α-In 2 Se 3 with a strain gradient when it is subjected to bending, rippling, or bubbling deformations to create localized ferroelectric domains with varying sizes. The switching dynamics depends on the magnitude of curvature and temperature, following an Arrhenius-style relationship. This study not only provides a promising solution for cross-scale studies using deep learning but also reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering.