The chain of thought （CoT） prompts enable large language models to process complex tasks according to specific reasoning steps， allowing them to demonstrate stronger capabilities in common sense reasoning， mathematical logic reasoning， and interpretability. However， the main drawback of the CoT approach lies in its reliance on massive language models， which typically have billions of parameters and face challenges in large-scale deployment. To address this issue， this paper proposes a large model knowledge distillation method based on the CoT， aiming to fully leverage the thinking and reasoning capabilities of large language models. Through knowledge distillation techniques， the main goal is to guide smaller models in solving complex tasks.This study adopts a large model as the teacher model and a small model as the student model， fine-tuning the student model by acquiring reasoning data from the teacher model. Through a series of carefully designed methods， such as changing data generation methods， clustering-based sampling of question-answer examples， heuristic correction of examples， and adaptive generation of answers， this study makes the generation process of the teacher model more efficient， resulting in higher-quality and larger quantities of reasoning data. This enables better fine-tuning of the student model， allowing it to acquire strong reasoning capabilities and achieve efficient knowledge distillation. The framework of this study aims to establish an effective knowledge transfer mechanism， allowing the deep thinking of large models to effectively guide smaller models， providing more intelligent and efficient solutions for solving complex tasks. Through this approach， we hope to overcome the challenges of deploying large models and promote the application and advancement of language models in the real world.