Analysis Model of Learning Chinese as a Foreign Language Based on Random Forest Algorithm

This article aims to propose an analysis model for learning Chinese as a foreign language based on the random forest algorithm. This model aims to analyze students’ learning situation and predict their learning performance by collecting learning data, including learning behavior, test scores, learning progress, etc. The random forest algorithm was chosen as the foundation of the model due to its strong learning and prediction abilities. By extracting features from the collected data and training the model, the model can accurately evaluate students’ language proficiency and provide personalized learning suggestions and feedback. This method extracts features from four dimensions: basic features, part of speech features, hierarchical features, and grammatical features. The random forest algorithm has potential in improving students’ learning effectiveness and personalized teaching, providing useful reference and support for teaching Chinese as a foreign language.