Abstract: Ovarian cancer is a kind of malignant gynecological tumor with inconspicuous early symptoms and complex lesion morphology. Ovarian tumor images can reflect the morphological, structural and histological information of lesions, and in-depth analysis of these features can improve the accuracy of ovarian cancer prognosis prediction. Existing methods tend to ignore the synergistic effect of local spatial, structural and texture information when processing ovarian tumor images, and lack holistic perception of uncertain lesion information, leaving room for improvement in the predictive performance and stability of models.This paper proposes an ovarian cancer prognosis prediction model named SPT-BUAT (Space-Structure-Texture Pseudo-Temporal Bayesian Uncertainty-Aware Transformer) based on spatial feature enhancement and Bayesian uncertainty-aware optimization. The model leverages space-structure-texture complementary model (SST-CM) to boost the representation of local and global image features, and employs BUAT to estimate and optimize the uncertain information of lesions.Experimental results demonstrate that the proposed SPT-BUAT model outperforms existing methods in ovarian cancer prognosis prediction and achieves more accurate discrimination of blurred boundaries and gray abnormal regions. Further visual analysis of SPT-BUAT verifies the consistency between the attention regions of the model and the actual lesion locations, which provides a new research perspective for the application of deep learning in ovarian tumor image analysis.