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The research of ontology-based similarity calculation between concepts has already been a hot issue of information technology in the fields of information retrieval, and so on. In this paper, the contents of the study is to find a fast and efficient mapping algorithm for heterogeneous ontologies in the same field. This paper put forward a method of similarity calculation based on heterogeneous ontologies, considering the factors of similarity of literal meaning and semantic structure (including the depth of the node, node density, edge weight, information content, etc.) can get concept mapping between heterogeneous ontologies more accurate. Simultaneously, taking into account the optimization of mapping method, the speed of matching has also been improved to a large extent. the problem of how to improve the speed of matching more effectually has been mentioned in this paper. The experiment results show this method can effectively get better effectiveness with concept similarity computing, excluding the effects of heterogeneous ontologies.