Concerning the problem that traditional aspect rating prediction methods just pay attention to textual information while ignoring the structural information in the review network, a novel Aspect rating prediction method based on Heterogeneous Information Network and Topic model (HINToAsp) was proposed for effectively integering textual information and structural information. Firstly, a new review topic model of opinion phrases called Phrase-PLSA (Phrase-based Probabilistic Latent Semantic Analysis) was put forward to integrate textual information of reviews and ratings for mining aspect topics. And then, considering the rich structural information among users, reviews, and items, a topic propagation model was designed by the aid of constructing "User-Review-Item" heterogeneous information network. Finally, a random walk framework was used to combine textual information and structural information effectively, which insured an accurate aspect rating prediction. The experimental results on both Dianping corpora and TripAdvisor corpora demonstrate that HINToAsp is more effective than recent methods like the Quad-tuples PLSA (QPLSA) model, the Gaussian distribution for RAting Over Sentiments (GRAOS) model and the Sentiment-Aligned Topic Model (SATM), and has better performance on recommendation system.