This paper proposes a Dynamic Sampling-based Non-local Correlation Matching model (DSNC-Match) for semi-supervised semantic segmentation. Traditional methods based on ResNet have focused on the extraction of local feature information, neglecting the effective mining of global information. Although Transformer models have garnered widespread attention for their superior ability to capture global information, they are associated with significant computational costs. In contrast, the DSNC-Match model proposed in this paper builds upon the strengths of ResNet in local feature extraction. Through innovative structural design, this new model not only enhances the capability to capture global information but also effectively mines the interrelated information between pixels and the latent information within historical models, all while maintaining a lower computational cost.