Existing Data Augmentation (DA) methods face key limitations: traditional approaches like Easy Data Augmentation (EDA) may introduce semantic distortions, while Pretrained Language Models (PLMs) often generate insufficiently diverse data. To address these challenges, we propose a Large Language Model (LLM) based framework that integrates EDA, PLM-based augmentation and LLM-based generation to produce the augmented data. Our method incorporates a reranking mechanism to filter generated samples, ensuring semantic consistency while maintaining diversity. Additionally, we utilize EDA and PLM-generated data as prompts to guide the LLM, leveraging its capabilities to further enrich the augmented data. Experiments demonstrate that our framework significantly improves the performance, achieving accuracy gains of 3.48 and 0.50 on the STSA and SNIPS datasets.