Objective：To investigate the value of a model based on multi -parameter magnetic resonance imaging（MRI）radiomics features in predicting the expression status of human epidermal growth factor receptor-2（HER -2）in invasive breast cancer patients. Methods：A retrospective analysis was conducted on baseline MRI images and clinical data of 401 breast cancer patients from January 2018 to December 2019 at the First Affiliated Hospital of Nanjing Medical University. The 2D region of interest（ROI）were segmented manually on ITK-SNAP software at the maximum tumor level of turbo inversion recovery magnitude（TIRM），dynamic-contrast enhanced magnetic resonance imaging phase 2（DCE2），dynamic -contrast enhanced magnetic resonance imaging phase 4（DCE4）， diffusion-weighted imaging（DWI）and apparent diffusion coefficient（ADC）. Feature extraction and dimensionality reduction screening were performed on the delineated ROIs. Logistic regression（LR）algorithm was used to construct single-parameter，combination and multi-parameter models for predicting HER-2 expression status. Results：A total of 26 optimal radiomics features were selected，with DCE2_original_shape_SurfaceVolumeRatio ranking first in terms of weight. Among the single-parameter models，the DCE2 model showed the best prognostic efficacy（AUC values of 0.907 and 0.879 for the training and test sets，respectively）. Among the combined models，the combined with DCE model had better predictive performance than the models without DCE（all P values ≤0.001）. The multi-parameter model had the best predictive performance（AUC values of 0.932 and 0.906 for the training and test sets， respectively）. Conclusion：The radiomics model based on multi-parameter radiomics features has certain clinical value in evaluating the HER-2 expression status in invasive breast cancer，with higher predictive value for early enhancement features.