Knowledge graphs is a semantic network to reveal the relationships between entities， which is often expressed in the form of Resource Description Framework （RDF）. Faced with the explosive growth of information， diversity semantic query requirements are ignored by the existing semantic query algorithm on RDF graphs. Therefore， considering the rich semantic information of RDF graphs， a Diversity Semantic Query method with distributed processing based on multi-level Neighborhood Predicate Label Tree Encoding index （NPLTE） on RDF graphs （DSQ-NPLTE） was proposed. Firstly， to avoid wasting storage space and assist subsequent parallel queries， a frequency-based predicate encoding and mapping strategy was designed to map the predicates represented by long strings to unique natural number representation. Secondly， after partitioning the RDF graph， the obtained vertices were classified according to their adjacent edge properties， and the corresponding storage modes were given. Thirdly， a multi-level NPLTE was proposed to filter invalid vertices and edges by the use of predicate feature information. Finally， for diversity semantic queries with known predicate， known subject （object） and known mixture， the corresponding matching strategies were given， and an optimal connection based on common vertex was proposed to reduce Cartesian product number and thereby decreasing the cost of connection. Experimental results show that compared with the method without preprocessing， the query efficiency of the proposed method can be improved by 5 to 9 times through using the constructed index for pruning optimization； compared with FAST method on three LUBM standard synthetic datasets of different sizes， the proposed method has the query efficiency improved by 43% on average. It can be seen that the proposed index and query strategy can deal with diversity semantic queries on large-scale RDF graphs effectively.