Tumor calcification refers to the phenomenon of calcium salt deposition in tumor tissues. In pathological sections, the analysis of the proportion of calcified areas is of great significance for the benign and malignant classification of tumors, monitoring of disease progression, tracking of treatment effects, and precision medicine such as surgery and radiotherapy. Compared with traditional pathological sections, fully digital pathological sections have advantages such as high-quality image preservation, remote access and sharing, and multidimensional data analysis. The integration of artificial intelligence algorithms for automated image segmentation, feature extraction, and data computation makes pathological assessment more efficient and accurate. The areas of tumor calcification areas in pathological sections are diverse and distributed discretely. Doctors typically need to manually estimate the proportion of calcified areas, which is time-consuming and imprecise. To address this issue, this study investigated an intelligent assessment system for tumor calcification areas based on fully digital pathological sections. The system utilizes the ECR-UNet network that integrates the attention modules to achieve precise segmentation of calcification areas. Edge detection technology is employed to segment the outer sections of pathological sections. The areas of both regions are then calculated separately to determine the proportion of calcified areas. The improved network demonstrates good segmentation performance on the test set, with the Dice coefficient, accuracy, and precision reaching 89.13%, 98.94%, and 90.81%, respectively. A comparison with the gold standard set by doctors for segmented calcified areas, outer contour areas, and the proportion of calcified areas on pathological sections reveals average accuracies of 92.25%, 99.05%, and 91.76%, respectively. This method provides an effective tool for the intelligent assessment of tumor calcification areas, assisting pathologists in tumor calcification diagnosis.