The essence of recommender systems is to model the implicit preferences in consumer behavior. The human behavior is inseparable from psychology, and there are rich internal motives behind the superficial behavior. However, the current studies mainly focus on the behavioral data modeling, rarely involve the internal psychological activities and the information processing process in decision-making. Therefore, this paper studied a new idea of recommender systems by introducing AIDMA decision model from the perspective of consumer decision journey. This paper proposed a new deep review-based recommender system, which applies the AIDMA decision journey into the deep learning framework. Experiments showed that the recommendation performance of the proposal is significantly better than the state-of-the-art methods. This paper follows the big data-driven research paradigm of "model driven + data-driven", realizing the in-depth method innovation with theoretical support.