Helpfulness prediction task of online reviews is significant in the contemporary e-commerce environment. It aims to evaluate the helpfulness of online reviews and then highlight the reviews more helpful to future consumers, thereby improving the consumers’ efficiency in obtaining information. This study concentrates on the new multidimensional scoring system emerging on various online platforms in recent years, and tries to study the influence of aspect ratings given by users in the system on the helpfulness of online reviews. To accomplish the helpfulness prediction task, it puts forward a multi-level neural network model HORA that considers all three components of review texts, overall ratings, and aspect ratings, as well as their interconnections. The experimental results on two real-world datasets show that HORA outperforms the present baseline models in terms of MAE and RMSE and exhibits good robustness. This indicates the significance of aspect ratings for the helpfulness awareness of users’ online reviews.