This paper introduces a two-stage deep learning approach to enhance the performance of single-channel target speech extraction. Conventional discriminative models often over-optimize objective metrics， leading to unnatural artifacts， distortions， and compromised subjective auditory quality in the extracted speech. To address this limitation， we innovatively incorporate a diffusion model as a second-stage refinement module. This module， built upon a stochastic differential equation （SDE） framework， regenerates and refines the initial output from the discriminative model. This process not only reduces extraction errors but also significantly improves the naturalness and harmonic clarity of the target speech. Experimental results on the WSJ0-2mix-extr dataset show that our method achieves an 11.18% improvement （from 3.22 to 3.58） on the non-intrusive speech quality assessment （NISQA） metric， which simulates subjective auditory perception. This indicates a substantial enhancement in perceived speech quality and naturalness. Furthermore， subjective listening tests using the comparative mean opinion score （CMOS） confirm the effectiveness of our approach in boosting speech clarity and intelligibility.