In the face of intelligent demand for information search, knowledge search is now the most promising and has been widely used in the current knowledge graph research. However, the traditional search methods based on node label only are difficult to present the semantic relationships between multidimensional nodes due to that the structure information and information in other dimensions is neglected, thus resulting in low semantic relevance and search efficiency for query results. In this paper, in order to improve the semantic relevance and quality of search results, we propose a knowledge searching algorithm based on analysis of multidimensional semantic similarity for knowledge graph systems, which combines both the ontology information and multi-hop neighborhood information together during the search process. The algorithm is designed for a knowledge graph system developed by the State Grid Anhui power distribution network, China. Several experiments are performed and the results show that the proposed algorithm outperforms the recent knowledge searching methods.