Heart sound auscultation is an effective diagnostic method for early screening of heart disease. In order to improve the performance of abnormal heart sound detection， this paper proposes a heart sound classification algorithm based on bi-directional long short-term memory （Bi-LSTM） network and self-attention mechanism （SA）. Firstly， the heart sound signal is partitioned into frames， and the Mel-frequency cepstral coefficients （MFCC） features are extracted from each frame of the heart sound signal. Next， the MFCC feature sequence is input into the Bi-LSTM network to extract the temporal contextual features of the heart sound signals. Then， the weights of the features output from the Bi-LSTM network at each time step are dynamically adjusted through self-attention mechanism， and more discriminative heart sound features that are conducive to classification are obtained. Finally， the Softmax classifier is used to classify normal/abnormal heart sounds. The proposed algorithm is evaluated using 10-fold cross-validation on the heart sound dataset provided by PhysioNet/CinC Challenge 2016， and achieves sensitivity of 0.942 5， specificity of 0.943 7， accuracy of 0.836 7， F₁ score of 0.886 5， and accuracy of 0.943 4， respectively， which are superior to typical comparative algorithms. Experimental results show that the proposed algorithm can effectively detect abnormal heart sounds without the need for heart sound segmentation， and has potential clinical application prospects.