Addressing the weakness of the single spectral representation, an algorithm is proposed for point pattern matching on the basis of multiple spectral representations. The eigenvalue series obtained by various matrix representations of graphs are used as the descriptor of feature point. The similarities between the given local structural descriptors are evaluated via the technique of multiview spectral embedding. Combined with the geometric consistency, point pattern matching problem is solved by using the method of probabilistic relaxation. Comparative experiments conducted on both synthetic data and real images verify the effectiveness and robustness of the proposed method.