Aiming at the problems that the dimensions of vectors generated by Traditional Vector Space Model （TVSM） are high， and the vector dot product operation to calculate the correlation between the documents and the queried keywords is time-consuming， a Dictionary Partition Vector Space Model （DPVSM） for ciphertext ranked search in cloud environment was proposed. Firstly， the specific definition of DPVSM was given， and it was proved that the relevance score between the queried keywords and the documents in DPVSM was exactly the same as that in TVSM. Then， by adopting the equal-length dictionary partition method， an encrypted vector generation algorithm and a relevance score calculation algorithm between documents and queried keywords were proposed. Experimental results show that the space occupation of document vectors of DPVSM is much lower than that of TVSM， and the more the number of documents， the greater the occupation reduction. In addition， the space occupation of query vectors and the time consumption of relevance score calculation are also much lower than those of TVSM. Obviously， DPVSM is superior to TVSM in both the space efficiency of generated vectors and the efficiency cost of relevance score calculation.