As electricity market reform deepens, the participation of end users in long term electricity transactions is intensifying, and their price perception and demand response behavior have become critical determinants of resource allocation efficiency. To address the limited capability of conventional bidding models to represent dynamic user behavior and to handle continuous decision spaces, a bilateral bidding strategy optimization framework is developed that integrates a multi period price perception mechanism with the deep deterministic policy gradient (DDPG) algorithm. A nonlinear price perception model with a Sigmoid structure is first established, in which cycle adjustment factors and stochastic perturbations are introduced to characterize response thresholds and behavioral heterogeneity under different price regimes. On this basis, a DDPG based policy learning architecture is constructed, and a differentiated reward function is designed to jointly account for profit maximization, social welfare improvement, and market fairness enhancement, thereby enabling adaptive optimization in a continuous action space. The proposed framework is evaluated through numerical simulation studies on representative market scenarios. The results indicate that market revenues can be increased, load modulation and temporal shifting can be promoted, and a favorable trade off among multiple operational objectives can be achieved. Moreover, user behavior is captured with higher fidelity, generator bidding strategies are more efficiently optimized, and the stability and social welfare of the electricity market are enhanced.