This paper conducts an in-depth study on the problem of shortcut learning in convolutional neural networks for predicting rolling bearing faults, and proposes a physics-guided convolutional neural network model for fault diagnosis and prediction. Using rolling bearing datasets, this study analyzes the shortcut learning problem that occurs during the training of CNN-based rolling bearing fault diagnosis models, and reveals the existence of the shortcut learning phenomenon: even though the convolutional network achieves an accuracy of over 90% on a specific fault dataset, due to the presence of shortcut learning, the model fails to learn the correct fault features that match the fault theory. Instead, it learns incorrect characteristic frequencies or waveform patterns in the spectrogram. The study also analyzes the generation mechanism of the shortcut learning phenomenon in fault diagnosis, and reveals the generation mechanism. Shortcut learning behavior in convolutional neural networks mainly arises from shortcut opportunities in the dataset, caused by factors such as background noise and assembly, the model's tendency to learn simple feature combinations, and data statistical biases caused by comprehensive errors. Since the fault dataset itself cannot sufficiently constrain the learning of deep neural network models, this paper designs sensitive frequency bands based on bearing fault characteristics according to the characteristic frequencies of rolling bearings. It generates physics-guided data through band-pass filters, constructs physics-guided information, and inputs it into the convolutional neural network model to guide the model to learn correct fault features. Experimental verification shows that the physics-guided convolutional neural network can effectively avoid the shortcut learning problem, accurately extract core fault features, improve the accuracy of fault diagnosis and prediction, and enhance the credibility of the convolutional network-based fault diagnosis model. It has application prospects in fault diagnosis of high-end equipment in fields such as aerospace.