This paper proposes an objective evaluation model of stereo image visual perception based on convolutional neural network (CNN) and support vector regression (SVR) to solve the issue of multidimensional influencing factors for stereo images and improve the accuracy of prediction results. In this proposed model, the plane saliency map using color and the disparity map based on differences are combined, and the potential salient discomfort regions of visual perception are obtained using threshold segmentation. Then, global features, such as contrast, color, and structural complexity, are extracted using feature extraction along with the local features, such as disparity, texture, and spatial frequency. Finally, the objective evaluation model of multifeature visual perception is constructed by combining CNN and SVR to obtain the final objective prediction value. Experimental results show that the Pearson linear correlation coefficient and Spearman's rank correlation coefficient of the proposed method are higher than 0.87 and 0.83, respectively. In addition, compared with other existing methods, the objective evaluation model proposed in this paper is better on the public dataset, and the prediction results have higher consistency with the subjective evaluation results.