With the continuous development of knowledge graph technology， knowledge information management driven by knowledge graph has been widely applied in multiple domains， so the efficiency of distributed Simple Protocol and Resource description framework Query Language （SPARQL） query for knowledge graph is particularly important. Firstly， a detailed investigation on the existing Spark-based and Random Access Memory （RAM）-based distributed RDF systems was conducted. Secondly， query performance evaluation of eight representative systems selected from the above systems was performed， thereby comparing query performance differences between Spark-based and RAM-based systems with different query types， query diameters and datasets. Thirdly， the query performance of Spark-based and RAM-based systems was evaluated by analyzing the experimental results comprehensively. Finally， the future research directions of distributed SPARQL query optimization which oriented vertical application domain were pointed out aiming at problems of the existing distributed SPARQL query， such as poor query scalability， high query join complexity and long query compilation time.