Automatic analysis of cholecystectomy surgical videos has significant clinical value. However, current models are limited to simple tasks like single-frame phase recognition and multi-tool classification, failing to effectively utilize video context for complex clinical reasoning. They lack the ability to integrate medical textual knowledge with cholecystectomy images and long surgical videos. We propose CholecMamba, a model that compresses video feature sequences through the Mamba architecture and deeply integrates with large-scale reasoning language models to achieve multimodal reasoning capabilities for surgical videos. Our main contributions include: 1) Designing a novel architecture that enables visual feature compression and knowledge feature injection, supporting multi-task video analysis of varying lengths; 2) Innovatively incorporating segmentation category information generated by large language models into the decoder, enhancing surgical video understanding and reasoning segmentation capabilities through medical knowledge logical reasoning; 3) Proposing the Surgical Reasoning Synthesis method, which leverages physician annotations and reinforcement learning with large language models to create the CholecReason dataset containing 49K multi-round dialogues, establishing a new benchmark for surgical video understanding and reasoning segmentation. Experimental results demonstrate that our model achieves optimal performance on existing datasets and CholecReason, with a closed-test score of 0.822, significantly outperforming the best competing model’s score of 0.728. Our code is available at https://github.com/displaywz/CholecMamba .