With　the　widespread　adoption　of　deep　learning,　the　performance　of　Visual　Question　Answering　(VQA)　tasks　has　seen　significant　improvements.　Nonetheless,　this　progress　has　unveiled　significant　challenges　concerning　their　credibility,　primarily　due　to　the　susceptibility　of　linguistic　biases.　Such　biases　can　result　in　considerable　declines　in　performance　when　faced　with　out-of-distribution　scenarios.　Therefore,　various　debiasing　methods　have　been　developed　to　reduce　the　impact　of　linguistic　biases,　where　causal　theory-based　methods　have　attracted　great　attention　due　to　their　theoretical　underpinnings　and　superior　performance.　However,　traditional　debiased　causal　strategies　typically　remove　biases　through　simple　subtraction,　which　neglects　the　fine-grained　bias　information,　resulting　in　incomplete　debiasing.　To　tackle　this　issue,　we　propose　a　fine-grained　debiasing　method　named　as　VQA-PDF,　which　utilizes　the　features　of　the　base　model　to　guide　the　identification　of　biased　features,　purifying　the　debiased　features　and　aiding　the　base　learning　process.　This　method　has　shown　significant　improvements　on　VQA-CP　v2,　VQA　v2　and　VQA-CE　datasets.