Heart sound signal is an important signal for analyzing and diagnosing heart problems， and heart sound segmentation is an essential part before analyzing and processing it. By separating the heart sound segmentation task into two sub tasks of localization and recognition， this paper proposes a two-stage convolutional neural network， which is composed of localization network and discrimination network to complete the recognition and localization of heart sound signals respectively. First， the original signal is divided into frames through a sliding window， then the spectrum is obtained by short time Fourier transform， and then the Mel frequency spectral coefficient（MFSC） characteristics are obtained by Mel filter. The first localization model is input to judge whether it is a heart sound segment. If so， the discrimination neural network is input to identify the first heart sound and the second heart sound， so as to achieve heart sound segmentation. At last， multi frame voting results are used to reduce the misjudgment. At the same time， the spatial attention mechanism is introduced into the convolutional neural network. Experimental results show that this two-stage neural network model with attention mechanism has higher accuracy in heart sound segmentation tasks than a single convolutional neural network classification model， and also makes the model more simple and lightweight.