Existing studies have achieved automatic coding of classroom dialogue and the mining of learning patterns based on the results of automatic coding. However, they have failed to focus on conducting automatic coding research and data mining analysis on classroom dialogue in native language education. In view of the technological advantages of large language models, this study proposes a strategy that combines the construction of training data and model fine-tuning to achieve automatic coding of classroom dialogue in the field of Chinese native language education. Through large-scale coding, data analysis is carried out to extract learning patterns, and then educational experiments are conducted to verify the effectiveness of these learning patterns. The empirical results show that this study has improved the efficiency of automatic coding of classroom dialogue in Chinese native language education, achieved in-depth mining of subject teaching patterns, and effectively contributed to the improvement of learning outcomes in the classroom.