Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving.Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationshipbetween documents will be normally concealed in the process of encryption, which will lead to significant search accuracyperformance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it evenmore challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on largevolume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics andalso to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clustersthe documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until theconstraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computationalcomplexity against an exponential size increase of document collection. In order to verify the authenticity of search results, astructure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection setbuilt from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of theproposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, theproposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.