To address the issues of ignoring constraint information and the curse of dimensionality in long-path reasoning in Knowledge Graph Question Answering （KGQA）， a Knowledge Graph constrained Question Answering based on Hierarchical Reinforcement Learning （HRL） （KGQA-HRL） model was proposed. Firstly， the concept of HRL was integrated deeply， triples in the knowledge graph were decomposed， and a high-level policy as well as a low-level policy was designed， so as to mitigate the curse of dimensionality risk in reasoning paths. Secondly， to improve the accuracy of path selection， an attention-based action selection strategy and an entity selection strategy incorporating constraint information were introduced， thereby narrowing the search space for reasoning effectively. Thirdly， a question update phase was embedded between the action selection and entity selection strategies， thereby enabling secondary update of the question at each hop. Finally， in the entity selection strategy， a constraint set was constructed and constraint scores were calculated， so as to incorporate constraint information from the question， thereby enhancing the accuracy of entity selection. Experimental results on four KGQA benchmark datasets to evaluate the performance of KGQA-HRL model demonstrate that KGQA-HRL model achieves the optimal accuracy on all datasets， with an average improvement of 2.9% over the previous best model reinforcement learning based COnstrained PAth Reasoning （COPAR）. At the same time， KGQA-HRL model has outstanding performance in complex three-hop query tasks （3.6% improvement on the PQ （PathQuestion） dataset and 2.5% improvement on MetaQA dataset）， validating good reasoning capability of KGQA-HRL model.