As one of the most spoken languages in the world, there is a growing demand for teaching Chinese. Under the background of information-based education, learners’ learning behavior data are increasingly abundant, which contain important information such as learners’ learning habits, interest preferences, and learning effects. This study aims to use data mining techniques to extract valuable information from massive learning data, reveal the behavioral patterns of Chinese learners, and then guide the teaching practice to improve the teaching effect and learners’ satisfaction. In this paper, we adopt data mining technology, including data preprocessing, feature extraction, model construction, and result verification steps, to conduct in-depth analysis of learners’ behavioral data and reveal their behavioral patterns. Finally, based on the mining results, a learner behavior pattern analysis model is constructed and applied to the practice of Chinese language teaching. This study finds that Chinese learners’ behavioral patterns show certain regularities and differences. This study not only provides new perspectives and methods for Chinese language teaching but also helps to promote the development of education informatization. In the future, with the continuous development and improvement of data mining technology, its application in Chinese language teaching will be more extensive and in-depth, injecting new vitality into the innovation and development of Chinese language education.