Semi-supervised semantic segmentation methods typically employ various data augmentation schemes to ensure differentiation in the input of network branches, enabling mutual self-supervision. While successful, this approach faces several issues: 1) insufficient diversity in feature extraction leads to feature signal assimilation during inference; 2) inadequate diversity in supervision signals results in the assimilation of loss learning. These issues cause network branches to converge on similar solutions, degrading the functionality of multi-branch networks. To address these issues, a cross semi-supervised semantic segmentation method based on differential feature extraction is proposed. First, a differential feature extraction strategy is employed, ensuring that branches focus on distinct information, such as texture, semantics, and shapes, thus reducing reliance on data augmentation. Second, a cross-fusion pseudo-labeling method is introduced, where branches alternately generate neighboring pixel fusion pseudo-labels, enhancing the diversity of supervision signals and guiding branches toward different solutions. Experimental results demonstrate this method achieves excellent performance on the Pascal VOC 2012 and Cityscapes validation datasets, with scores of 80.2% and 76.8%, outperforming the latest methods by 0.3% and 1.3%, respectively.