Aiming at the problem that the non-semantic features of reviews cannot be fully utilized in opinion spam detection, a hierarchical attention mechanism and heterogeneous graph attention network based model, Hierarchical Heterogeneous Graph Attention Network (HHGAN), was proposed. Firstly, the hierarchical attention mechanism was used to learn the word-level and sentence-level document representations to focus on the capturing of the words and sentences that were important to the opinion spam detection. Then, the learned document representations were used as nodes, and the non-semantic features in reviews were selected as meta-paths to construct a heterogeneous graph attention network with a double-layer attention mechanism. Finally, a Multi-Layer Perceptron (MLP) was designed to distinguish the categories of reviews. Experimental results on datasets of restaurant and hotel extracted from yelp.com show that the F1 values of the HHGAN model reach 0.942 and 0.923 respectively, which are better than those of the traditional Convolutional Neural Network (CNN) model and other benchmark models of neural network.