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Abstract The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system. Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation. However, the vibration signal usually contains noise in real working conditions, which raises concerns about accurate recognition of cavitation in noisy environment. This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment. First, we train a convolutional neural network (CNN) using the spectrogram images transformed from raw vibration data under different cavitation conditions. Second, we employ the technique of gradient‐weighted class activation mapping (Grad‐CAM) to visualise class‐discriminative regions in the spectrogram image. Finally, we propose a novel image processing method based on Grad‐CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image. The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments. The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal‐to‐noise ratios of 4 and 6 dB, respectively.