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Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials (PFMs). This paper presents a method for the automatic recognition of bubbles in transmission electron microscope (TEM) images of W nano-fuzz using image processing techniques and convolutional neural network (CNN). We employ a three-stage approach consisting of Otsu, local-threshold, and watershed segmentation to extract bubbles from noisy images. To address over-segmentation, we propose a combination of area factor and radial pixel intensity scanning. A CNN is used to recognize bubbles, outperforming traditional neural network models such as AlexNet and GoogleNet with an accuracy of 97.1% and recall of 98.6%. Our method is tested on both clear and blurred TEM images, and demonstrates human-like performance in recognizing bubbles. This work contributes to the development of quantitative image analysis in the field of plasma-material interactions, offering a scalable solution for analyzing material defects. Overall, this study’s findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions. This method can be employed in a variety of specialties, including plasma physics and materials science.