Pulsars, rapidly rotating neutron stars, have been crucial topics in astrophysics since their discovery. The advent of deep learning offers a novel method for the efficient classification of pulsar candidates. However, the extreme imbalance between positive and negative classes in the dataset severely affects the performance of the classification model. Therefore, a network enhancing detail perception and stability (EDPSNet) is proposed to address class imbalance. The model combines Deep Convolutional Generative Adversarial Networks (DCGAN) with multi-scale Channel-Spatial Attention (CSA) modules and Spectral Normalization (SN). To enhance local details and global features, thereby enhancing the overall quality and detail of generated images, we introduced a self-designed CSA module. To enhance the stability of the model, SN stabilizes training and prevents mode collapse by constraining the weight matrices of the network. In addition, we proposed the Pixel Moving Segmentation (PMS) technique to address the overfitting issue in generative models caused by insufficient samples. This method expands the dataset while preserving the original features, thus increasing the sample diversity. Experimental results indicate that the EDPSNet model and PMS technique offer significant advantages in handling class imbalanced, small-sample pulsar candidate datasets.