Aiming at the problems of the existing Graph Neural Network （GNN）-based collaborative filtering methods under sparse and noisy data conditions， such as the obscuring of true signals by static noise injection， the inability of fixed semantic prototypes to capture dynamic user interests， and the high computational overhead of complex augmentation， a graph diffusion generation and adaptive sampling-based contrastive collaborative filtering method was proposed. Firstly， a lightweight graph diffusion generation mechanism based on gradual denoising was designed， so as to optimize node representations through forward noise-adding and reverse denoising， thereby generating noise-resistant contrastive views. Then， random masking was integrated with Random Walk with Restart （RWR） to model local neighborhood features and global structural semantics collaboratively， thereby generating high-quality negative samples. Finally， an improved InfoNCE （Information Noise Contrastive Estimation） loss function was introduced to optimize the multi-view contrastive learning objective and enhance the discriminative power of representations. Experimental results on Gowalla， Yelp， and Amazon datasets show that compared to the best-performing baseline method， the proposed method improves the Top-20 Recall （Recall@20） by 0.63%， 1.36%， and 1.88%， respectively， and the Top-40 Normalized Discounted Cumulative Gain （NDCG@40） by 0.95%， 1.47%， and 1.24%， respectively， as well as improves the recommendation performance for long-tail users by 26.7%， increases the training efficiency by 90%， and accelerates the convergence speed by 32%. It can be seen that the proposed method enhances the noise resistance and dynamic adaptability of recommendation systems in open environments significantly.