The scarcity of abnormal samples of electronic connectors makes it difficult for supervised models to capture abnormal sample features, which largely limits the detection performance of supervised learning methods. Moreover, the existing unsupervised models have the problems of blurred reconstructed images and defects remaining, which seriously affect the detection accuracy. To this end, a one-step denoising simplex denoising diffusion probabilistic model electronic connector anomaly detection method that requires only anomaly-free sample training is proposed. Since Gaussian denoising diffusion probabilistic model has feature projection error in the image reconstruction task that leads to reconstruction position deviation, simplex noise is introduced to construct a simplex noise denoising diffusion probabilistic model, and the denoising paradigm is reformulated so that the inference time is reduced to 0.09 s. In addition, the research obtains an image preprocessing method that eliminates the interference of redundant features, so that the model learns the surface features of the electronic connector efficiently and improves the model learning efficiency. model learning efficiency. The experimental results show that the proposed method significantly outperforms the existing unsupervised models under the AUROC criterion, a standard evaluation metric for anomaly detection. The image-level detection accuracy reaches 99.71% and the pixel-level accuracy reaches 93.86%, demonstrating excellent anomaly detection performance.