Personalized dialogue systems are widely recognized for generating responses that reflect specific personas. However, existing approaches predominantly rely on predefined persona information, which not only requires substantial upfront manual annotation efforts but also struggles to adapt to the dynamic changes in persona. To address these issues, we propose Persona Extraction and Integration (PEI), a two-stage framework based on Large Language Models (LLMs) and LoRA fine-tuning. This framework aims to dynamically capture and integrate persona from dialogue history without predefined persona information, thereby optimizing the effectiveness of personalized dialogue generation. Experimental results show that PEI outperforms baseline models on both Chinese and English personalized dialogue datasets, confirming its superiority in personalized generation tasks.