In order to improve the under-utilization of low-confidence pseudo-labels， the need to optimize the accuracy of high-confidence pseudo-labels and the imbalance of pseudo-label categories in the semi-supervised semantic segmentation task of colorectal cancer pathological images， this paper proposed a pseudo-label confidence regulation method to achieve high-quality multi-class semi-supervised semantic segmentation of colorectal cancer pathological images. First， based on the semi-supervised semantic segmentation framework of the teacher-student model， we propose to embed class confidence regulation in the consistency regularization， and to enhance the certainty by removing the confusing classes in the low confidence pseudo-labels generated by the untrained teacher model， so as to increase the contribution rate of the low confidence pseudo-labels. Secondly， an operation paradigm of first screening and then refining the pseudo-tags generated by the teacher model after training is proposed. By refining the filtered high-confidence pseudo-tags based on conditional random fields， the problems of boundary ambiguity and lack of semantic information in high-confidence pseudo-tags are improved. Finally， in order to alleviate the category imbalance in pseudo-label data， an adaptive random cascade strong data enhancement method based on the classification number of pseudo-label is designed. Through the experimental verification of the self-built colorectal cancer pathological image dataset and the published multi-class pathological image dataset， the proposed method achieves 74.09% average segmentation accuracy of four categories of colorectal cancer pathological images， which is 6.43% higher than that of the benchmark network， and provides powerful algorithm support for semi-supervised semantic segmentation of colorectal cancer pathological images.