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Accurate segmentation of post-operative glioma poses a critical task in medical image processing due to the importance of evaluating therapeutic regimens and guiding the subsequent treatment. High annotation difficulties and costs exacerbate the difficulty of segmentation. Consequently, training an end-to-end segmentation model from scratch using annotated clinical data is infeasible. In addition, although foundation models have made significant progress in medical image segmentation, their direct application to post-operative glioma segmentation still faces challenges. In this paper, we propose a method to simulate post-operative giloma based on pre-operative data resection, and introduce a strategy to fine-tune a foundation model using simulated post-operative data. Post-operative glioma generation strategy combines noise-disturbed polyhedron and level set model. Specifically, the noise-disturbed polyhedron is utilized to simulate the residual cavity, while the level set is employed to mimic the control of resection levels among physicians with varying levels of experience. During the foundation model segmentation phase, a fine-tuning strategy incorporating the gray-level distribution of the tumor region for prompt optimization is taken into account. We evaluated the proposed method on two public post-operative glioma datasets and one private dataset, achieving improvements in the dice coefficient by 15.1%, 8.2%, and 14.2%, respectively, compared to state-of-the-art methods.Clinical Relevance- Our methodology facilitates precise localization of postoperative residual tumors by physicians, offering pivotal insights for the formulation of radiotherapy, chemotherapy, and subsequent follow-up protocols, thereby aiding in clinical decision-making and treatment strategies.