Object navigation requires an embodied agent to locate a target object in an unknown environment through visual observations. Existing zero-shot methods typically leverage open-vocabulary perception and semantic priors to identify promising search regions. However, these methods often assume that semantic cues provide reliable guidance, while lacking mechanisms to assess and adjust their influence during exploration. Consequently, misleading semantic evidence can persist throughout navigation, biasing frontier selection and causing agents to repeatedly explore unreliable regions. To address this problem, we propose SER-Nav, a training-free framework that achieves dynamic Semantic Evidence Regulation via relational bias. SER-Nav introduces dual relational biases: activation reinforces target-related and contextual evidence to guide exploration, while inhibition attenuates misleading evidence caused by perceptual confusion and failed verification. These relational biases dynamically update the navigation search space, and a reliability-aware commitment gate prevents premature pursuit of insufficiently verified targets. By adaptively adjusting the influence of semantic cues, SER-Nav preserves useful guidance while mitigating persistent semantic misguidance under noisy open-vocabulary perception. Experiments on standard ObjectNav benchmarks demonstrate that SER-Nav consistently improves success rate and path efficiency over representative zero-shot methods. Extensive ablation studies further validate the effectiveness of each component. Real-world robot experiments further validate its robustness and practical applicability.

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