Fully Homomorphic Encryption (FHE) enables machine learning models to operate directly on encrypted data, ensuring privacy-preserving analytics for sensitive user behavior information. However, the computational overhead of FHE raises concerns about efficiency in real-time applications. In this study, we evaluate three representative tree-based classifiers on encrypted user behavior data. The experiments are conducted under different computation depths and quantization bit-widths to examine their influence on accuracy and inference latency. Our results show that while all three models can be executed within the FHE framework, XGBoost consistently outperforms the others, achieving superior predictive accuracy with inference times suitable for near real-time analysis. These findings indicate that XGBoost offers the most effective balance between accuracy and efficiency for privacy-preserving user behavior classification.