The flexible use of prior experience to guide future behavior is a core feature of biological intelligence. However, the neural mechanisms that support such adaptive learning, especially in multi-goal environments, remain insufficiently understood. A hierarchical optimization navigation model driven by hippocampal reverse replay was proposed. The model was built on the spatial representation of place cells. Task-specific memory was formed through synaptic weight modulation induced by reverse replay. Cross-task optimization was achieved by loss regulation based on replay duration, thereby enabling the construction of a transferable navigation strategy. Unlike traditional retraining-based approaches, the proposed method indirectly optimizes the initial parameters of the model by adjusting the duration of replay, which allows rapid transfer of task knowledge. The model was systematically validated through simulations of mouse-like exploration in environments with dynamically changing goals. The results demonstrate that navigation time to known targets is significantly reduced, while high generalization performance to previously unseen goal locations is maintained. These findings confirm the effectiveness of the reverse-replay-based hierarchical optimization strategy for rapid multi-task adaptation. From the interdisciplinary perspective of neuroscience and computational modeling, this study provides new insights for the development of adaptive navigation systems in artificial intelligence and offers a theoretical basis for understanding and simulating hippocampal function.