In this paper, we propose a novel dual-stage self-evolution (DPSE) framework that overcomes the limitations of pre-training and improves the performance of large-scale language models (LLMs). Unlike existing post-training strategies that focus on improving user alignment, DPSE simultaneously optimizes user preference adaptation and domain-specific expertise. This is achieved by extracting multidimensional interaction signals through a censoring module and estimating satisfaction scores, and augmenting structured data through topic-aware and preference-based strategies. The augmented dataset supports a two-stage fine-tuning pipeline: supervised domain-based tuning and frequency-aware preference optimization. Experimental results on common NLP benchmarks and long-term conversation tasks show that DPSE outperforms supervised fine-tuning, preference optimization, and memory augmentation baseline models, and the contributions of each module are verified through ablation studies. In conclusion, the DPSE framework provides an autonomous path for continuous self-evolution of LLMs.