Abstract: Individual behavior critically regulates transmission dynamics during epidemic spread. However, existing coupled models often fail to fully integrate the effects of multiple realistic factors on individual behavioral decision-making or adequately characterize complex states (e.g., epidemic resurgences) in transmission processes. To address these gaps, this study proposes a bidirectional dynamic coupling model of behavioral decision-making and epidemic spread based on a two-layer network. Built on the SIS（Suspective-Infected-Suspective） epidemic framework, the model employs scaling factors to modulate epidemic infection and recovery rates via individual behaviors. Meanwhile, integrating social pressure, behavior implementation costs, and perceived disease risks within a game-theoretic framework, dynamic evolutionary rules for individual behavioral decisions are established to achieve bidirectional dynamic coupling between behavioral decision-making and transmission processes. Compared with existing two-layer network models, the proposed model significantly reduces the steady-state level of epidemic spread, enhances epidemic eradication capacity, and better captures complex dynamical behaviors (e.g., oscillations in transmission). For this highly nonlinear coupled system, theoretical stability analysis of multiple equilibria (including disease-free equilibrium and endemic equilibrium ) is conducted by combining Lyapunov stability theory with potential game methods, and numerical simulations validate the theoretical findings. Further sensitivity analysis of key parameters reveals the interaction laws between behavioral factors and epidemic spread, providing an important theoretical basis for formulating more effective epidemic intervention strategies.