The rapid development of the semantic web makes the various fields in smart city have emerged in the form of ontology to express the knowledge model. However, in the practical semantic Web application, it is often faced with the problem of lack of ontology instance. It is an extremely effective solution to transform the data in the existing relational data source into ontology instance, which requires the use of the relational model to the ontology model matching technology to establish the mapping between the data source and the ontology. In addition, the schema matching to the ontology model is widely used in data integration, data semantic annotation, ontology-based data access, and other fields. The existing related work tends to use a variety of schema matching algorithms to calculate the similarity of element pairs in heterogeneous data patterns. However, when multiple matching algorithms fail at the same time, it is difficult to obtain a more accurate final matching result. In this study, the weekness of the matching of the single schema matching algorithm are analyzed deeply, the localization feature of the data source is an important factor leading to this phenomenon, and an iterative optimization schema matching scheme is proposed. The scheme uses the matched element pairs from matching process to optimize the single schema matching algorithm. The optimized algorithm can be better compatible with the localization features of the data source, with much higher accuracy, and more matching elements can be obtained. The process continues to iterate until the end of the match. In this study, experiments are carried out through a practical case in the fields of "food information management" which have shown that the proposed approach significantly outperforms state-of-the-art method by increasing up to 50.1% of F-measure.