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Existing heart sound classification algorithms based on convolutional neural networks have the disadvantages of relying on precise segmentation of basic heart sounds, single classification model structure, and poor universality. So a method of training deep convolutional neural networks using a large number of two-dimensional heart sound feature maps that have not been accurately segmented is proposed. Firstly, the heart sound signal is preprocessed by the sliding window method and the Mel frequency coefficient to obtain a large number of heart sound feature maps that have not been accurately segmented. Then the deep CNN model is used to train and test the heart sound feature maps. According to the different connection modes between convolutional layers, three deep CNN models are designed: convolutional neural network based on single connection, convolutional neural network based on skip connection, and convolutional neural network based on dense connection. The experimental results show that the convolutional neural network based on dense connections has greater potential than based on single or skip connection. Compared with other heart sound classification algorithms, the algorithm we proposed does not rely on precise segmentation of basic heart sounds and has improved the accuracy, sensitivity and specificity of classification.