Abstract: Existing RDF keyword search methods mainly search on the large-scale RDF data graph directly and do not make full use of the semantic information in the RDF ontology. Too many iterations lead to unfavorable search efficiency and unsatisfactory results. To solve these problems, a distributed keyword approximate search algorithm (DKASR) for RDF based on Redis memory database cluster was proposed and the parallel search of large-scale data on the distributed platform was realized. The algorithm constructs ontology sub-graphs by using the semantic information of RDF ontology, uses the semantic scoring function to sort ontology sub-graphs, and searches and returns the Top-k results concurrently with the aid of MapReduce computation model. If the results do not meet Top-k, ontology sub-graphs are extended to generate approximate ontology sub-graphs and the semantic similarity function is used to sort approximate ontology sub-graphs. Then, MapReduce computation model was used to realize the parallel search until the results meet Top-k. Finally, the results of experiments show that the DKASR algorithm can realize the RDF keyword approximate search and return the Top-k results efficiently and accurately.