Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI-driven analytics due to the complexity of action dynamics and the lack of structured tactical representations. To address this, we present BoxMind, a closed-loop AI expert system validated in elite boxing competition. By defining atomic punch events with precise temporal boundaries and spatial and technical attributes, we parse match footage into 18 hierarchical technical-tactical indicators. We then propose a graph-based predictive model that fuses these explicit technical-tactical profiles with learnable, time-variant latent embeddings to capture the dynamics of boxer matchups. Modeling match outcome as a differentiable function of technical-tactical indicators, we turn winning probability gradients into executable tactical adjustments. Experiments show that the outcome prediction model achieves state-of-the-art performance, with 69.8% accuracy on BoxerGraph test set and 87.5% on Olympic matches. Using this predictive model as a foundation, the system generates strategic recommendations that demonstrate proficiency comparable to human experts. BoxMind is validated through a closed-loop deployment during the 2024 Paris Olympics, directly contributing to the Chinese National Team's historic achievement of three gold and two silver medals. BoxMind establishes a replicable paradigm for transforming unstructured video data into strategic intelligence, bridging the gap between computer vision and decision support in competitive sports.

翻译：竞技体育需要精细的战术分析，然而由于动作动态的复杂性以及缺乏结构化的战术表征，拳击等格斗类项目在人工智能驱动的分析领域仍发展不足。为此，我们提出了BoxMind——一个在精英拳击比赛中得到验证的闭环人工智能专家系统。通过定义具有精确时间边界、空间及技术属性的原子击打事件，我们将比赛录像解析为18个层次化的技战术指标。随后，我们提出了一种基于图的预测模型，该模型将这些显式的技战术特征与可学习的时变潜在嵌入相融合，以捕捉拳手对阵的动态特性。通过将比赛结果建模为技战术指标的可微函数，我们将获胜概率梯度转化为可执行的战术调整。实验表明，结果预测模型取得了最先进的性能，在BoxerGraph测试集上准确率达到69.8%，在奥运会比赛上达到87.5%。以此预测模型为基础，系统生成的战略建议展现出与人类专家相当的专业水平。BoxMind通过在2024年巴黎奥运会期间的闭环部署得到验证，直接助力中国国家队取得了三金两银的历史性成就。BoxMind建立了一个可复现的范式，能够将非结构化视频数据转化为战略情报，从而弥合了竞技体育中计算机视觉与决策支持之间的鸿沟。