The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic solvents and air pollutants disrupt the potassium, sodium, and calcium ion channels cardiac cell membranes, leading to the dysregulation of cardiac function. However, current cardiotoxicity models have disadvantages of incomplete data, ion channels, interpretability issues, and inability of toxic structure visualization. Herein, an interpretable deep-learning model known as CardioDPi was developed, which is capable of discriminating cardiotoxicity induced by the human Ether-à-go-go-related gene (hERG) channel, sodium channel (Na_v1.5), and calcium channel (Ca_v1.5) blockade. External validation yielded promising area under the ROC curve (AUC) values of 0.89, 0.89, and 0.94 for the hERG, Na_v1.5, and Ca_v1.5 channels, respectively. The CardioDPi can be freely accessed on the web server CardioDPipredictor (http://cardiodpi.sapredictor.cn/). Furthermore, the structural characteristics of cardiotoxic compounds were analyzed and structural alerts (SAs) can be extracted using the user-friendly CardioDPi-SAdetector web service (http://cardiosa.sapredictor.cn/). CardioDPi is a valuable tool for identifying cardiotoxic chemicals that are environmental and health risks. Moreover, the SA system provides essential insights for mode-of-action studies concerning cardiotoxic compounds. Environmental ImplicationHazardous substances in the environment and materials used in daily life have been reported to exhibit clear cardiotoxicity. Emissions of chemical pollutants and organic compounds can affect air quality and damage heart health. However, most predictive models of cardiac toxicity have shortcomings in terms of environment and materials. The present study focuses on the potential cardiotoxicity of compounds targeted on hERG, Cav1.2, and Nav1.5 channels in the environment and materials using deep learning neural network methods. We developed CardioDPi to predict the cardiotoxicity of compounds and detected structural alerts. The tools are freely available via http://cardiodpi.sapredictor.cn/ and http://cardiosa.sapredictor.cn/.