## 1 Introduction  
Premium threaded connections (PTCs) play a vital role in preventing the leakage of high-temperature, high-pressure, and hydrogen sulfide gases from tubing into the atmosphere [1, 2, 3]. The interference fit between the threads creates a gas-tight seal, but even minor damage during transportation or improper connections can compromise this seal, leading to environmental damage and economic losses. Therefore, diagnosing the connection quality of PTCs is crucial to prevent leaks. The current primary method involves the manual inspection of torque curves generated during PTC connections to identify damage or correct connections [4, 5, 6, 7]. However, manual inspection relies heavily on the professional background and work experience of field engineers, leading to a lack of objectivity and quantifiability in the evaluation [8]. Therefore, achieving intelligent diagnosis of PTCsquality necessitates automatic classification and recognition of PTCs curves under different connection conditions. In recent years, artificial intelligence (AI) applications and research across various industries have significantly improved the diagnosis of PTCs quality. [PERSON] et al. [9] introduced a machine learning method based on logistic regression for PTCs curve classification, achieving an initial intelligent recognition capability by categorizing curves into qualified and unqualified classes. Although this method reveals statistical and spatial feature distribution characteristics under varying conditions, it relies heavily on manual feature extraction. This dependency becomes a challenge, as improper feature extraction can considerably affect the model's classification outcomes. In contrast, deep learning through multi-layer neural networks automatically learns high-level feature representations from raw data, eliminating the laborious process of manual feature engineering [10]. Thus, the application of deep learning for the automatic classification of PTCs curves emerges as a promising solution to the current issues. Recently, considerable research on and applications of deep learning has been conducted for tasks such as target classification and recognition in areas including medicine, biology, and mechanical engineering. The use of convolutional neural networks (CNNs) has gained particular attention due to their unique convolutional operations and global weight-sharing mechanism, achieving state-of-the-art classification performance [11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. [18] proposed a deep learning-based intelligent method for monitoring and diagnosing the working conditions of sucker-rod pumping wells. This approach used deep convolutional neural network algorithms to analyze the relationship between electrical data and dynamometer cards under different working conditions, artificially synthesizing dynamometer cards. The researcher also employed convolutional neural networks to automatically diagnose the artificially synthesized dynamometer cards, achieving prediction accuracy exceeding 95%. [PERSON] et al. [19] combined convolutional neural networks and transfer learning methods. The pretrained network model was trained on a dynamometer card dataset with ten different working conditions, leading to the intelligent diagnosis of dynamometer card conditions. The recognition accuracy reached 92.6%, outperforming general fully connected neural networks and shallow convolutional neural networks. [PERSON] et al. [20] applied a convolutional neural network model to classify and recognize the original vibration data, frequency spectrum data, and time-frequency data of gearboxes. The results demonstrated that the neural network model could automatically learn key features from frequency domain data and intelligently diagnose gearbox faults. However, the aforementioned research on classification and recognition tasks based on convolutional neural networks typically assumes the availability of ample training data.