The subgraph isomorphism problem is a Non-deterministic Polynomial （NP）-complete problem， and the pivoted subgraph isomorphism is a special subgraph isomorphism problem. There are many existing efficient subgraph isomorphism algorithms， but there is no GPU-based search algorithm for the pivoted subgraph isomorphism problem at present， and a large number of unnecessary intermediate results will be generated when the pivoted subgraph matching problem is solved by the existing subgraph isomorphism algorithms. Therefore， a GPU-based pivoted subgraph isomorphism algorithm was proposed. Firstly， through a novel coding tree method， nodes were encoded by the combination of node labels， degrees and the structural features of node neighbors. And the query graph nodes were pruned on GPU in parallel， so that the size of search space tree generated by the data graph candidate nodes was significantly reduced. Then， the candidate nodes of the query graph node were visited level by level， and the unsatisfied nodes were filtered out. Finally， the obtained subgraph was verified whether it was an isomorphic subgraph of the query graph， and the search of pivoted subgraph isomorphism was realized efficiently. Experimental results show that compared with GPU-friendly Subgraph Matching （GpSM） algorithm， the proposed algorithm has the execution time reduced by one-half， and the proposed algorithm can efficiently perform the pivoted subgraph isomorphism search with scalability. The proposed pivoted subgraph isomorphism algorithm can reduce the time required to solve the pivoted subgraph isomorphism problem， while reducing GPU memory consumption and improving the performance of algorithm.