This paper proposes a respiratory pattern recognition method for anxiety disorders based on an SVM-XGBoost-LR stacked ensemble model, enabling objective screening and early monitoring of anxiety disorders through the analysis of respiratory signals. A portable respiratory monitoring system was designed and implemented based on the ESP32 microcontroller and SM9541 high-precision pressure sensor, capable of real-time acquisition of oral-nasal airflow signals and data transmission via WiFi. In the algorithmic processing, Kalman filtering was employed for signal denoising, combined with a three-stage feature selection strategy (VIF-ANOVA-RF) to extract key respiratory features, along with the introduction of a Transformer self-attention mechanism for feature enhancement. The proposed SVM-XGBoost-LR stacked ensemble model achieved an overall classification accuracy of 96% on a dataset containing 600 samples, and improved the F1-score for the minority class (simulated breath-holding) from 60% to 87%, effectively alleviating the issue of missed diagnoses of abnormal patterns caused by class imbalance. Experimental results indicate that this method demonstrates superior performance in identifying anxiety-related respiratory abnormalities, providing a reliable algorithmic foundation and technical pathway for the development of portable mental health monitoring devices.