Existing feature extraction methods struggle with low accuracy in strong noise and small sample scenarios, affecting parametric fault diagnosis in DC-DC converters. To address this issue, we propose an Adaptive Euler Difference Feature Extraction (AEDFE) method that extracts spatial features from fault signals to enhance the differentiation between parametric fault features of varying severity. This approach is implemented through a simple convolutional neural network, achieving high-precision diagnosis of DC-DC converter parametric faults even in challenging conditions. Experimental results demonstrate that the proposed AEDFE achieves 100% diagnostic accuracy in strong noise environments, with an average improvement of 61.61% compared to three other methods. Additionally, when training data is reduced to 10% of the original, the method still maintains an accuracy of 99.98%, representing a 77.64% increase in diagnostic precision compared to the comparative methods. These findings effectively demonstrate the superior performance of AEDFE in strong noise and small sample environments.