Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models always lack interpretability. In this study, we developed eight interpretable deep learning models to predict respiratory toxicity of chemicals, focusing on specific respiratory diseases such as pneumonia, pulmonary edema, respiratory infections, pulmonary embolism and pulmonary arterial hypertension, asthma, bronchospasm, bronchitis, and pulmonary fibrosis. In addition, we integrated data from eight respiratory toxicity endpoints into a comprehensive dataset and developed an overall respiratory system model. Model performance was evaluated using 5-fold cross-validation and external validation, with area under the curve (AUC) and accuracy (ACC) values exceeding 0.85 for all eight toxicity endpoints. To enhance model interpretability, we employed the frequency ratio method to identify key structural fragments in Klekota-Roth fingerprints (KRFP) and utilized SHAP (SHapley Additive exPlanations) game theory analysis to visualize critical features driving model predictions. This study demonstrates the role of interpretable deep learning models in predicting the respiratory toxicity of drugs and their environmental metabolites, offering valuable tools and information for early detection and risk assessment of pharmaceutical compounds and environmental pollutants with respiratory toxicity potential.