This study proposes a deep learning recommendation algorithm based on attention mechanism to solve the problem that the current recommendation algorithms based on comment texts have insufficient extraction of text features and implicit information. The comment text representations of users and items are constructed, and the context dependency of texts is extracted by bidirectional gated recurrent units for text feature representations. Moreover, the attention mechanism is introduced to obtain the interest preference of users and the attribute features of items more accurately. The two sets of hidden features of the generated user and item comment data are respectively input into the fully connected layer and then merge into the same vector space for rating prediction. As a result, the recommendation results are obtained. Experiments on two public data sets, Yelp and Amazon, show that the proposed algorithm has better recommendation performance than other algorithms.