In order to match and analyze effectively of the structured graph in the database, we present an improved
Kuhn-Munkres algorithm based on the global similarity matrix and location similarity matrix. Firstly, we
construct a global matrix and a location matrix from graph data. The global similarity matrix is constructed
with the adjacency matrix and the Laplacian spectrum of feature structures. As for the location similarity
matrix, we use the Gaussian kernel function to perform the normalized calculation of the relative position
and construct the matrix with the spectral features. The nodes location similarity matrix describes the
relative position of all nodes in the graph, which can offset the drawback of the global similarity matrix,
which means it can only depict the overall structure of the graph. Finally, we use the Kuhn-Munkres algorithm
to do graph matching, and get the maximum weight matching in bipartite graphs. Experiments show that the
improved Kuhn-Munkres algorithm can effectively enhance matching accuracy between nodes.