The drawbacks of the traditional visual comfort assessment metrics for stereoscopic images by using only global disparity features were analyzed. An objective visual discomfort prediction model of stereoscopic images was proposed based on visual saliency analysis. Firstly, an image saliency map and a depth saliency map were calculated by using covariance matrices and Sigma feature sets respectively according to the stereo visual attention mechanism of human eyes and the stereoscopic saliency map was obtained by combination of the two calculations. Then, visual discomfort perceptual features were obtained by using the stereoscopic saliency map as weighting. Finally, the relationship between the visual discomfort perceptual features and the subjective scores was established by constructing a visual discomfort prediction function with support-vector regression, and the objective visual comfort scores were predicted. Experimental results show that the Pearson Linear Correlation Coefficient (PLCC) index of the proposed method reaches 0.79, and the Spearman Rank Order Correlation Coefficient (SRCC) index reaches 0.81. These results indicate that the proposed model can achieve higher consistency with subjective perceptual of stereoscopic images, and is more consistent with human visual systems.