Large Language Models (LLMs) perform excellently in fake news detection tasks, but their outputs are often accompanied by hallucinations, i.e., generated content that is contradictory to facts. Previous studies have mostly mitigated hallucinations through prompt design. However, this paper reveals that regions in news articles which easily induce hallucinations in LLMs correspond closely to the most challenging regions for fake news detectors. In this paper, we propose a fake news detection framework (PHPFND) based on post-hoc processing of LLMs hallucination. Specifically, our framework includes a hallucination detection module (ISHD) based on information structuring that detects three types of hallucinations in LLMs in a targeted manner, and a hallucination-driven feature enhancement mechanism (HDFE) that incorporates hallucination signals as explicit features into sentence-level encoding and feature fusion to guide the model’s attention toward high-risk regions. Experimental results on two mainstream fake news datasets show that our proposed method significantly outperforms LLM-based baselines.