Scanning electron microscopy (SEM) imaging can visually reveal the microscopic world. In SEM imaging, the device parameters must be repeatedly adjusted to ensure the optimum image contrast. This process is often time-consuming and labor-intensive. We propose a novel no-reference quality assessment method for evaluating the SEM image contrast distortion based on multi-scale characteristics, which can be used as a guide to select imaging parameters. Firstly, a SEM image database is established, and the corresponding subjective mean opinion score (MOS) is obtained via subjective experiments. According to the multi-scale characteristics of the human visual system, 10 features are extracted, including singular value decomposition similarity with different scales, frequency domain features, and entropy. The MOS values and 10 features are then used to train a regression model via support vector regression. Finally, this model is used to predict the image quality score. The experimental results reveal that the proposed method can maintain a high level of consistency with subjective evaluation results, and its performance is superior to the mainstream full-reference and no-reference quality assessment methods.