Abstract: As an important component of rotating machinery, the normal operation of rolling bearings directly affects the service life and operating status of the machine. Aimed at improving the accuracy of fault diagnosis for rolling bearings, a flexible residual neural network (FResNet) based on dynamic subtraction average based optimizer (DSABO) and parallel attention module (PAM) is proposed for rolling bearing fault diagnosis to address this problem. Specifically, a flexible residual module based on convolutional neural networks is first designed to construct FResNet. This module allows for changing the numbers of convolutional layers, convolutional kernels, and skip connections during DSABO iterations, thereby enhancing fault feature extraction capabilities and reducing network degradation. Secondly, a PAM with convolutional layers is designed to integrate the output weights of channel attention and spatial attention, and achieve data feature enhancement by combining it with rolling bearing operation data. Thus, the integration of DSABO, PAM, and FResNet forms an effective rolling bearing fault diagnosis model, named DSABO-PAM-FResNet. Finally, the feasibility and effectiveness of the proposed DSABO-PAM-FResNet model are validated using the rolling bearing fault dataset from Case Western Reserve University in the United States. The experimental results show that the accuracy of the proposed model in diagnosing rolling bearing faults in an environment with a signal-to-noise ratio of -6 dB is 97.18 %, proving that the proposed model has good noise resistance ability. Under different load conditions of 1 hp, 2 hp, and 3 hp, the average accuracy of the proposed model in diagnosing rolling bearing faults is 98.2 %, proving that the model has good adaptability to variable working condition diagnosis. The comparison results with other intelligent fault diagnosis methods show that the proposed DSABO-PAM-FResNet model has higher diagnostic accuracy, providing a new and effective intelligent method for rolling bearing fault diagnosis.