Cabin cables, as critical components of an aircraft’s electrical system, significantly impact the operational efficiency and safety of the aircraft. The existing cable segmentation methods in civil aviation cabins are limited, especially in automation, heavily dependent on large amounts of data and resources, lacking the flexibility to adapt to different scenarios. To address these challenges, this paper introduces a novel image segmentation model, CableSAM, specifically designed for automated segmentation of cabin cables. CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy. It accurately segments cables in various scenarios with minimal input prompts. Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.