Objective This study aimed to develop a longitudinal deep learning ultrasound model to achieve two objectives： non-invasive prediction of breast cancer molecular subtypes prior to neoadjuvant therapy（NAT） and identification of the optimal timing for early efficacy assessment during NAT. Methods We enrolled 176 breast cancer patients from Guangzhou First People's Hospital who completed the full NAT course. The cohort was stratified into two analysis subsets： 176 patients for molecular subtyping and 167 for treatment response evaluation. Pathological data and serial ultrasound images were collected. Tumors were categorized into four molecular subtypes via immunohistochemistry. Treatment response was classified as "significant" or "non-significant" based on postoperative Miller-Payne grading. We employed a hybrid U-Net-EfficientNet-B0 architecture integrated with a segmentation-guided attention mechanism （SegAttend-Net）. The model leveraged pre-treatment images and dynamic sonographic feature changes across early NAT stages to predict molecular subtypes and therapeutic response. Confidence intervals were calculated using Clopper-Pearson exact and Bootstrap methods. Performance metrics across treatment cycles were adjusted for multiple comparisons using the Benjamini–Hochberg procedure. Evaluation utilized confusion matrices and longitudinal performance trajectories. Results In molecular subtyping， the model achieved accuracies of 82% （Luminal A）， 88% （Luminal B）， 72% （HER2-overexpressing）， and 96% （triple-negative）. For efficacy prediction， overall accuracy increased from 71% at cycle 1 to 80% at cycle 4， while sensitivity improved markedly from 0.14 to 0.79. The sensitivity improvement between the 3rd and 4th cycles was statistically significant. Conclusions The developed SegAttend-Net model demonstrates efficacy in pre-NAT molecular subtyping and holds clinical value for early efficacy assessment， with optimal predictive performance observed at the fourth treatment cycle.