Deep learning has been successfully applied in the recommender system. Low-dimensional dense embedding is typically used to represent the feature of users and items. To optimize the model, some models propose to dynamically search the embedding size based on the popularity of different users and items. However, these models ignore the interaction between the user and the item which will hinder the optimization of the features in embedding. In this paper, we propose Object-aware Policy Network (OPN) and introduces an object-aware method that is used for optimizing the features in embedding. We evaluate our model on the two real-world benchmark datasets. With less than 10% increased time consumption in all experiments, the results show that our proposed model is able to improve the performance of binary classification task by a margin of 0.30 and multiclass classification task by a margin of 0.35 compared to the best accuracies achieced by baselines on different datasests.