In order to solve the problem that the daily rehabilitation training of cardiovascular patients depends on directing by health care professions in rehabilitation centre, during assessing and correcting the movements system about cardiovascular patients' independent rehabilitation training at home, this paper proposed an action assessment method for cardiovascular patients' rehabilitation training based on key points of the human skeleton(ASRT-PHS). Firstly, this paper constructed a dataset for rehabilitation training actions using a camera and data augmentation in accordance with the specified rehabilitation trai-ning specification for cardiovascular patients. Secondly, this paper employed a deep learning-based detector and pose estimator to capture human body positions and extract key points of the human skeleton, respectively, and then input the results into a convolutional neural network for action recognition. Thirdly, by calculating joint angle thresholds, joint distance ratio and assessing standard motions, this paper constructed a motion segmentation model based on joint distance ratios and an action assessment model based on action joint angle thresholds. This paper investigated the optimal combination of ASRT-PHS by assessing its performance with various joint angle thresholds and action recognition approaches. The results show that ASRT-PHS achieves an average action recognition, segmentation and assessment accuracy of 92.78%, 77.6% and 87%, respectively. Furthermore, case tests about the true cardiovascular patients show that the average accuracy of the prototype system is 71.3%, which provides a feasible intelligent auxiliary system for patients' autonomous rehabilitation training at home.