Objective To explore the diagnostic value of deep learning model based on calcification characteristic parameters for breast ductal carcinoma in situ (DCIS). Methods From Jan. 2016 to Dec. 2022, data of patients with calcification found by mammography in Shenzhen Hospital of Southern Medical University or Cancer Center of Sun Yat-sen University were retrospectively analyzed. According to the pathological diagnosis results, the patients were divided into benign group (569 cases) and DCIS group (263 cases). The calcification feature detection and classification model were established by deep learning, and the characteristic parameters with statistically significant differences were screened. The receiver operating characteristic curve (ROC) was used to analyze the diagnostic effectiveness of each characteristic parameter, the deep learning model and traditional machine learning models on DCIS. Results Among the 20 characteristic parameters, 5 (the number of linear branching-like calcification, the number of granular calcification, rate of segment distribution, rate of clustered distribution, and population density) had significant differences between the 2 groups, and the DCIS group had higher values (all P＜0.05). The area under curve (AUC) values of ROC in the number of linear branching-like calcification, the number of granular calcification, rate of segment distribution, rate of clustered distribution and population density for identifying DCIS were 0.762, 0.732, 0.725, 0.757 and 0.810, respectively, with sensitivities of 81.2%, 85.9%, 80.1%, 87.8% and 86.4%, and specificities of 76.0%, 71.6%, 70.3%, 73.4% and 63.7%, respectively. The AUC value, sensitivity and specificity of the deep learning model established by the combination of the 5 feature parameters for recognition of DCIS were 0.823, 89.3% and 77.3%, respectively, which were higher than those of independent feature parameters and traditional machine learning models, including support vector machine, K-nearest neighbor, linear discriminant analysis, and logistic regression (AUC values were 0.771, 0.801, 0.765, and 0.734, sensitivities were 79.7%, 80.9%, 77.8%, and 74.4%, and specificities were 57.8%, 62.9%, 56.6%, and 47.9%, respectively). Conclusion Screening the calcification features with high diagnostic value and establishing a deep learning model are helpful to the diagnosis of DCIS with mammograms.