In order to improve the explainability of Recommendation System （RS）， break the inherent limitations of recommendation system and enhance the user’s trust and satisfaction on recommender systems， a Deep Review Attention Neural Network （DRANN） model with enhanced explainability was proposed. Based on the potential relationships between users and items on text reviews， the rich semantic information in user reviews and item reviews was used to predict users’ interest preferences and sentiment tendencies by the proposed model. Firstly， a Text Convolutional Neural Network （TextCNN） was used to do shallow feature extraction for word vectors. Then， the attention mechanism was used to assign weights to comment data and filter invalid comment information. At the same time， the deep autoencoder module was constructed to reduce the dimension of high-dimensional sparse data， remove interference information， learn deep semantic representation， and enhance the explainability of recommendation model. Finally， the prediction score was obtained through the prediction layer. Experimental results on the four public data sets including Patio， Automotive， Musical Instrument （M?I） and Beauty show that DRANN model has the smallest Root Mean Square Error （RMSE） compared with Probabilistic Matrix Factorization （PMF）， Single Value Decomposition++ （SVD++）， Deep Cooperative Neural Network （DeepCoNN）， Tree-enhanced Embedding Model （TEM）， DeepCF （Deep Collaborative Filtering） and DER（Dynamic Explainable Recommender）， verifying its effectiveness in improving performance and the feasibility of the adopted explanation strategy.