In the study of personalized facial attractiveness, due to lack of features and insufficient consideration of the influence factors of public aesthetics, the prediction of personal preferences cannot reach high prediction accuracy. In order to improve the prediction accuracy, a new personalized facial attractiveness prediction framework based on feature-level and decision-level information fusion was proposed. Firstly, the objective characteristics of different facial beauty features were fused together, and the representative facial attractive features were selected by a feature selection algorithm, the local and global features of face were fused by different information fusion strategies. Then, the traditional facial features were fused with the features extracted automatically through deep networks. At the same time, a variety of fusion strategies were proposed for comparison. The score information representing the public aesthetic preferences and the personalized score information representing the individual preferences were fused at the decision level. Finally, the personalized facial attractiveness prediction score was obtained. The experimental results show that, compared with the existing algorithms for personalized facial attractiveness evaluation, the proposed multi-level fusion method has a significant improvement in prediction accuracy, and can achieve the Pearson correlation coefficient more than 0.9. The proposed method can be used in the fields of personalized recommendation and face beautification.