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Item recommendation has become a significant means to help people discover interesting items. Meanwhile, plenty of reviews and ratings in recommender system can be utilized to relieve data sparsity problem. However, existing review-based approaches ignore the influence of static preference of user and the static characteristics of item, which could reflect long-term and stationary property, and guide feature extraction from reviews. Moreover, adaptive property, i.e., the importance of the historical records to each user and item, is not fully exploited in previous works. In this paper, we propose an Attentionbased Adaptive Memory Network (AAMN) model to leverage historical reviews and ratings systemically. Specifically, we propose an attention mechanism guided by the static features to learn the importance of different historical records, for modeling the adaptive features of users and items. Notably, this paper is the first to bring static features into adaptively extracting semantic information from reviews, which can not only characterize user and item from a global view, but also assist to distinguish the importance of different reviews. In addition to the attention mechanism, we propose a non-linear feature fusing layer and a deep interaction layer to combine the static features and adaptive features, which capture underlying interactions among these features. To further improve prediction accuracy and training efficiency, we propose a dynamic sampling strategy for model training. We conduct extensive experiments on 16 benchmark datasets from Amazon and Yelp. The results demonstrate that our model outperforms the state-of-the-art models.