In HR+/HER2- early breast cancer (EBC) patients, approximately one-third of stage II and 50% of stage III patients experience recurrence, with poor outcomes after recurrence. Given that these patients commonly undergo adjuvant chemo-endocrine therapy (C-ET), accurately predicting the recurrence risk is crucial for optimizing treatment strategies and improving patient outcomes.We collected postoperative histopathological slides from 1095 HR+/HER2- EBC who received C-ET and were followed for more than five years at West China Hospital, Sichuan University. Two deep learning pipelines were developed and validated: ACMIL-based and CLAM-based. Both pipelines, designed to predict recurrence risk post-treatment, were based on pretrained feature encoders and multi-instance learning with attention mechanisms. Model performance was evaluated using a five-fold cross-validation approach and externally validated on HR+/HER2- EBC patients from the TCGA cohort.Both ACMIL-based and CLAM-based pipelines performed well in predicting recurrence risk, with UNI-ACMIL demonstrating superior performance across multiple metrics. The average area under the curve (AUC) for the UNI-ACMIL pipeline in the five-fold cross-validation test set was 0.86 ± 0.02, and 0.80 ± 0.04 in the TCGA cohort. In the five-fold cross-validation test sets, effectively stratified patients into high-risk and low-risk groups, demonstrating significant prognostic differences. Hazard ratios for recurrence-free survival (RFS) ranged from 5.32 (95% CI 1.86-15.12) to 15.16 (95% CI 3.61-63.56). Moreover, among six different multimodal recurrence risk models, the WSI-based risk score was identified as the most significant contributor.Our multimodal recurrence risk prediction model is a practical and reliable tool that enhances the predictive power of existing systems relying solely on clinicopathological parameters. It offers improved recurrence risk prediction for HR+/HER2- EBC patients following adjuvant C-ET, supporting personalized treatment and better patient outcomes.© 2025. The Author(s).