Multiple MapReduce tasks are needed for most of current distributed parallel reasoning algorithm for RDF data; moreover, the reasoning of instances of triple antecedents under OWL rules can't be performed expeditiously by some of these algorithms during the processing of massive RDF data, and so the overall efficiency can't be fulfilled in reasoning process. In order to solve the problems mentioned above, a method named distributed parallel reasoning algorithm based on Spark with TREAT for RDF data is proposed to perform reasoning on distributed systems. First step, alpha registers of schema triples and models for rule markup with the ontology of RDF data are built; then alpha stage of TREAT algorithm is implemented with MapReduce at the phase of OWL reasoning; at last, reasoning results are dereplicated and a whole reasoning procedure within all the OWL rules is executed. Experimental results show that through this algorithm, the results of parallel reasoning for large-scale data can be achieved efficiently and correctly.