Rolling bearings are critical and vulnerable components in mechanical equipment and are prone to various types of damage during operation. Consequently, rolling bearing fault diagnosis is of significant engineering importance. In recent years, deep learning-based approaches have achieved considerable progress in intelligent bearing fault diagnosis. However, existing models still suffer from several limitations, including insufficient feature extraction under noisy conditions, limited diagnostic accuracy, high computational cost, and low operational efficiency. To address these challenges, an intelligent rolling bearing fault diagnosis method based on an ascending-dimensional convolutional neural network (ADCNN) is proposed. Compared with conventional neural networks, the proposed ADCNN features a more compact model size, improved noise robustness, and higher diagnostic accuracy. A large convolutional kernel is introduced in the first layer to enhance noise immunity, while an ascending-dimensional module is employed to reduce the number of network parameters and improve feature extraction capability. In addition, a reduced linear transformation layer (RLTL) is incorporated to further achieve a lightweight architecture. Experimental results on the Case Western Reserve University (CWRU) dataset and a self-designed test dataset demonstrate that the proposed ADCNN achieves superior fault diagnosis performance under different noise environments while maintaining computational efficiency and model compactness.