Coronary artery disease (CAD), as one of typical heart diseases, threatens people’s lives and health. However, due to its complex influencing factors and its subtle initial symptoms, many patients miss the optimal treatment window for recovery. To enable early diereses prevention so as to get most appropriate treatment, many machine learning methods have been widely applied in this field, among which deep learning has been acknowledged as one of the cutting-edge techniques for CAD diagnosis. This paper develops a tailored BP neural network, which integrates BP with an attention-residual-mechanism for CAD detection. In order to find the key factors that contribute to CAD prediction, we fist investigate a feature selection strategy based on data visualization and using several statistical methods on the commonly used cleveland heart disease dataset. Then, the attention-residual-mechanism informed BP network is conducted for CAD detection. The amended BP network alleviates the gradient vanishing problem by using a residual structure and captures deep dependencies between features through a multi-head attention mechanism, which can be used for dynamic allocation of feature weights. Extensive experiments demonstrate the better performance of our method than existing machine learning algorithms. It can achieve an accuracy of 97.1% on Cleveland Heart Disease dataset, which verifies the effectiveness of our method in CAD diagnosis.