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This invention discloses a deep learning-based surgical quality assessment method and system, relating to the field of medical information. The method includes: acquiring preoperative three-dimensional images and generating a surgical operation space baseline map using a first deep neural network; continuously acquiring real-time instrument pose data and high-definition video frame sequences of the surgical area tissue during surgery; using a spatiotemporal attention mechanism for dynamic registration to construct an instrument-tissue interaction spatiotemporal feature set; calculating instrument operation standardization scores and tissue processing accuracy scores using a second deep neural network; acquiring postoperative physiological recovery indicators; inputting the two scores and indicators into a causal inference model to quantify their respective marginal contributions and generate a comprehensive quality assessment index; and outputting a graded assessment conclusion based on the comparison results of this index with multi-level thresholds. This invention achieves an objective, interpretable, and clinically predictive quantitative assessment of surgical quality by integrating data from the entire chain of preoperative planning, intraoperative operation, and postoperative recovery.