Numerically controlled (NC) machine tools, as vital production equipment in manufacturing, have been widely applied across various sectors and have become a core competitive advantage for enterprises in the global market. Therefore, ensuring the normal and efficient operation of NC machine tool groups and promptly diagnosing faults have become critical concerns for many enterprises and scholars today. This paper focuses on bearing fault diagnosis, utilizing the vibration signals from the Case Western Reserve University Bearing Data Center as the input dataset. This study constructed a dual-stream convolutional neural network (CNN) fault diagnosis model, where the first stream processes one-dimensional vibration signal spectra and the second stream handles two-dimensional time-frequency maps derived from the same signals. The model uniquely integrates convolutional attention mechanisms to enhance feature extraction along with dropout algorithms and batch normalization to prevent overfitting and improve training stability. The proposed approach enables a comprehensive learning of both temporal and spatial features, effectively identifying bearing faults with high accuracy. The model’s performance was validated against this widely recognized dataset, demonstrating superior accuracy compared to traditional methods.