Nuclear power is a clean energy source that plays an important role in the development of the modern economy. The outdoor pipeline of nuclear power plant is an important part of connecting each plant and system, and if it leaks, it may lead to nuclear accidents and important losses of personnel and property. The polyethylene pipeline (PE pipeline) is a commonly used material for outdoor pipeline. To date, there is no automated process to detect impurities such as sand, hair, grass, and plastic in PE pipe joint welds. Impurities in joint welds can lead to serious long-term leakage. To improve the safety and reliability of the weld quality of outdoor PE pipelines in nuclear power plants, this study proposes a novel automated quality diagnosis method for outdoor PE pipelines in nuclear power plants based on a convolutional neural network (CNN) algorithm, which is used to improve the reliability and traceability of pipeline weld quality detection. This work used a large dataset of PE pipe joint samples created in harsh outdoor environments, in contrast to past studies that mainly used datasets created under controlled and laboratory conditions. The results show that the accuracy of the impurity recognition rate of this model is 86.29%, and that the automation process allows the identification of quality defects faster than the manual inspection method. The model will continue to collect samples and improve accuracy.