The intelligent diagnosis of premium threaded connections (PTC) is crucial for ensuring the stability and sealing of oil pipes under high temperature, high pressure, and acidic gas conditions. Accurate diagnosis relies on analyzing PTC curves under different operating conditions to reflect the quality of the buckling, but obtaining a large amount of valid data in actual industrial inspections is challenging. This study introduces an end-to-end classification model that combines asynchronous optimized 2D deep convolutional generative adversarial network (AoT-DCGAN) and 2D convolutional neural network (P-CNN), aiming to improve classification performance with small sample sizes. The proposed method first utilizes AoT-DCGAN to identify the distribution pattern of original samples and generate corresponding synthetic samples. At the same time, a novel weight optimization strategy, asynchronous optimization (AO), is implemented to alleviate the gradient vanishing problem during the generator optimization phase. Subsequently, a novel P-CNN model is designed and trained on an expanded dataset to achieve automatic classification of PTC curves. The method is evaluated based on recall, specificity, F1 score, precision, and confusion matrix under different data augmentation ratios. The results indicate that as the dataset size increases, the model’s classification ability improves, peaking at a dataset size of 1200. In addition, within the same training set, the performance of the P-CNN model outperforms traditional machine learning and deep learning models, achieving optimal classification accuracies of 95.9%, 95.5%, and 96.7% on the AC, ATI, and NDT curves, respectively. Finally, research confirms that applying asynchronous optimization during the training process of DCGAN results in a more stable decrease in the loss function.