Abstract: Convolution neural networks (CNNs) have been widely used in the field of speech recognition to make up the deficiency of traditional acoustic models, such as weak robustness, poor real-time and low recognition performance. In this paper, the applicability and recognition performance of abnormal sound recognition based on CNNs were analyzed. Applied on 6 common abnormal sounds, we explored the dimension of sound signal features how to influence the performance of CNNs architecture, as reference methods, the Gaussian Mixture Model (GMM) and Back Propagation neural networks were employed to compare with CNNs algorithm. The experimental results reveled that 1D features produce higher error convergence rate and average accuracies with the relative increase of 2.91% in the noiseless environment. Nevertheless in noisy context, 2D features perform better, the relative increase reaches 3.41%. Meanwhile, CNNs method has distinct advantage in the terms of noise robustness and error convergence speed over other two approaches.