Evolutionary Algorithms （EAs） are widely adopted in Neural Architecture Search （NAS） due to their strong global exploration capability. However， traditional mutation ways often lack performance information guidance and have limited local exploitation capability， thereby constraining search efficiency and architecture quality. Therefore， this paper proposed a Diffusion-Guided Evolutionary NAS （DGEA） algorithm to enhance mutation quality and search capability jointly via distribution-aware and bias control mechanisms. The core of DGEA is to use a discrete diffusion model to generate structural perturbations， aiding evolutionary search process. By learning structural priors from the distribution of high-performance architectures， DGEA performed controlled noise addition and conditional denoising to create new high-performance architecture. At the same time， DGEA introduced an adaptive mutation strategy to balance exploration and exploitation dynamically. Besides， DGEA integrated a lightweight Harmony Search （HS） operator as local enhancement module for fine-tuning neighborhoods， thereby suppressing premature convergence. Experimental results on NAS-Bench-101， NAS-Bench-201， and ImageNet100 transfer tasks show that DGEA boosts search efficiency and effectiveness significantly. Under similar computational budgets， compared to EG-NAS （Neural Architecture Search with Fast Evolutionary Exploration）， GEA （Guided Evolutionary Neural Architecture Search with Efficient Performance Estimation） and REA （Regularized Evolution for Image Classifier Architecture Search）， DGEA reduces the architecture evaluations needed to reach the same accuracy level by 10%-84% with the overall accuracy gains of 0.28 to 0.42 percentage points. Ablation studies verify the contributions and synergistic effect of Diffusion-Guided Mutation （DGM） mechanism and HS module. It can be seen that by integrating distribution-aware generative mutation with adaptive HS local optimization， DGEA improves evolutionary search’s dynamic performance and convergence stability.