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Breast cancer is a serious threat to human life and health. Tamoxifen is a common treatment for breast cancer patients after surgery. However, patients still face the risk of recurrence or metastasis after treatment, so effective prognostic methods are needed to predict efficacy. In order to explore a method for prognostic analysis of postoperative tamoxifen treatment for breast cancer based on molybdenum target X-ray image. In this paper, the model for mammographic density extraction from molybdenum target X-ray image is studied by using the Squeeze-and- Excitation Convolutional Neural Network (SE-CNN) method. A prognostic imaging marker, mammographic density change rate (MDCR), was proposed and survival analysis was performed to study its prognostic ability for postoperative tamoxifen treatment of breast cancer. The results show that the threshold absolute error of SE-CNN is 9.92±4.78, and the determination coefficient is 0.77. The results show that this method can accurately extract the threshold. The progression-free survival was HR 2.654(95%CI,1.102-6.395), P =0.030.Patients with high MDCR had a better prognosis, while those with low MDCR had a worse prognosis. It is concluded that the rate of mammographic density change can be used as a prognostic imaging marker of postoperative tamoxifen treatment for breast cancer. [ABSTRACT FROM AUTHOR]