人工智能（AI）中知识蒸馏（KD）技术通过构建师生框架实现模型轻量化，成为解决深度学习性能与效率瓶颈的关键技术。本文从算法原理演进的视角，系统解析知识蒸馏的理论框架，将知识迁移路径归纳为基于响应、特征、关系及结构四类范式，并构建动态与静态知识蒸馏方法的对比评估体系。我们深入探讨了跨模态特征对齐、自适应蒸馏架构及多教师协同验证等创新机制，同时剖析渐进式知识迁移与对抗蒸馏等融合策略。通过计算机视觉与自然语言处理领域的实证分析，评估了该技术在图像分类、语义分割及文本生成等场景中的实用性。特别地，我们强调了知识蒸馏在农业与地学领域的潜力，例如在资源受限环境下的精准农业和地理空间分析中实现高效部署。研究发现当前模型普遍存在知识选择机制模糊、理论解释性不足等瓶颈问题。据此，我们探讨了自动化蒸馏系统与多模态知识融合等前沿方向的可行性，为边缘智能部署及隐私计算提供了新的技术路径，尤其适用于农业智能化与地学研究。

Knowledge Distillation (KD) in Artificial Intelligence (AI) achieves model lightweighting through a teacher-student framework, emerging as a key technology to address the performance-efficiency bottleneck in deep learning. This paper systematically analyzes KD’s theoretical framework from the perspective of algorithm evolution, categorizing knowledge transfer paths into four paradigms: response-based, feature-based, relation-based, and structure-based. It establishes a comparative evaluation system for dynamic and static KD methods. We deeply explore innovative mechanisms such as cross-modal feature alignment, adaptive distillation architectures, and multi-teacher collaborative validation, while analyzing fusion strategies like progressive knowledge transfer and adversarial distillation. Through empirical analysis in computer vision and natural language processing, we assess KD’s practicality in scenarios like image classification, semantic segmentation, and text generation. Notably, we highlight KD’s potential in agriculture and geosciences, enabling efficient deployment in resource-constrained settings for precision agriculture and geospatial analysis. Current models often face issues like ambiguous knowledge selection mechanisms and insufficient theoretical interpretability. Accordingly, we discuss the feasibility of automated distillation systems and multimodal knowledge fusion, offering new technical pathways for edge intelligence deployment and privacy computing, particularly suited for agricultural intelligence and geoscience research.