In recent years, Graph Convolution Network (GCN) has been widely applied in recommendation systems. Compared to traditional collaborative filtering methods that only aggregate information of neighboring nodes, GCN-based recommendation systems can aggregate information of high-order neighborhoods of a node to achieve better performance. However, GCN-based recommendation systems have faced the over-smoothing problem. The over-smoothing problem causes the performance of recommender systems first to rise and then decrease as the number of stacked layers increases. Previous GCN-based recommender systems have provided a few solutions to over-smoothing. We believe that there are still two problems: (1) most models focus on aggregating diverse information, with less consideration of filtering noise; (2) most models ignore the fact that there are differences in different stages of the process of aggregating information using GCN. We introduce a Phase-wise Attention mechanism to address these issues and propose a Phase-wise Attention GCN (PAGCN) recommendation model. Considering the different characteristics of different stages during the aggregation process of the GCN-based recommendation systems, our model uses different information aggregation methods in different graph convolution layers and adopts targeted aggregation methods. In this way, high-order neighborhood information can be more controlled to improve the performance and effectiveness of our model. The experimental results on real-world datasets show that our model outperforms various baselines, demonstrating the reasonableness of our method.