The traditional evaluation method of cardiac chest compression is based on acceleration waveform integration, which is affected by noise and integral delay. A large error is introduced in calculating the distance and the evaluation effectiveness is not ideal. Therefore, based on the weak supervised learning strategy and waveform segmentation, this paper proposes one kind of acceleration waveform recognition algorithm for cardiac chest compression based on onedimensional convolutional neural network. Experimental results show that the onedimensional convolutional neural network achieves 994% accuracy, which is significantly better than the traditional integration method and BP neural network algorithm. Further, the GradCAM method is adopted to visually analyze the evaluation results. The features of convolutional neural network focus on the acceleration waveform changes in the two compression stages of starting to press until the pressure can reach the equilibrium position. The reverse acceleration can achieve the maximum value after the pressing of the hand to the next press start. In addition, the evaluation model does not need to accurately measure the pressing distance. Thus, it is not affected by factors such as pressing occlusion and electromagnetic wave interference. Its effectiveness can be checked in the realtime manner. It also has the feature of high robustness in complex environment and has certain practical value in the field of medical emergency.