Skull identification is an important subject in forensic medicine. To solve the insufficient representation of skull and facial features in previous skull identification research, a skull identification method is proposed based on the fusion of view and shape features to fully use the effective recognition information of the skull and facial model and improve the skull recognition ability. First, a multi-view neural network is used to learn the multi-view features of skull and facial skin, the LS-MDS algorithm based on double harmonic distance is used to calculate the standard shape of skull and facial skin, and the pooling fusion method is used to aggregate multiple features to reduce the information loss in the view pooling stage. Then, to solve the problem of wave core features being sensitive to scale transformation, the scale invariant wave core features of skull and facial skin are extracted using feature value normalization. Finally, the view and wave core features are fused using kernel canonical correlation analysis to obtain the final feature vector of skull and facial skin. Skull identification is realized by calculating the correlation coefficient of the skull and facial skin feature vectors. Experiments show that the recognition accuracy of the proposed method is 95.4%, which is superior to other methods, thereby proving the effectiveness of the proposed skull identification method.