Pre-trained　language　models　(PLMs)　have　shown　remarkable　performance　on　question　answering　(QA)　tasks,　but　they　usually　require　fine-tuning　that　depends　on　a　substantial　quantity　of　QA　pairs.　Therefore,　improving　the　performance　of　PLMs　in　scenarios　with　only　a　small　number　of　training　examples,　also　known　as　a　few-shot　setting,　is　of　great　practical　significance.　Current　mitigation　strategies　for　the　few-shot　QA　task　largely　rely　on　pre-training　a　QA　task-specific　language　model　from　scratch,　overlooking　the　potential　of　foundational　PLMs　to　generate　QA　pairs,　particularly　in　the　few-shot　setting.　To　address　this　issue,　we　propose　a　prompt-based　QA　data　augmentation　method　aimed　at　automating　the　creation　of　high-quality　QA　pairs.　It　employs　the　prompt-based　fine-tuning　method,　adapting　the　question　generation　process　of　PLMs　to　the　few-shot　setting.　Additionally,　we　introduce　a　dynamic　text　filling　training　strategy.　This　strategy　simulates　the　progressive　human　learning　process,　thereby　alleviating　overfitting　of　PLMs　in　the　few-shot　setting　and　enhancing　their　reasoning　capability　to　tackle　complex　questions.　Extensive　experiments　demonstrate　that　the　proposed　method　outperforms　existing　approaches　across　various　few-shot　configurations.　　©　2020　IEEE.