We propose a novel knowledge enhancement zero-shot stance detection method, which employs LLMs to construct data augmentation based on Wikipedia’s background knowledge and uses Causal debiasing techniques to calibrate the LLM bias. First, the method retrieves, filters and summarizes relevant Wikipedia knowledge using topic modeling and LLMs, while enhancing text comprehension through LLM-based paraphrasing. Second, a logical Chain-of-Thought module is employed to generate coherent augmented data by deriving logical expressions from text-target pairs, in order to address the limitations, such as the lack of logical relevance and the insufficient generalization ability in data augmentation. Finally, the method introduces causal counterfactual debiasing theory with a calibration network to mitigate LLM biases while improving generalization. Extensive experimental results demonstrate that the proposed method achieves superior performance over the state-of-the-art baselines.