With the rapid development of e-commerce, online reviews have played an increasingly important role in consumers' shopping intentions and behaviors. Therefore, how to effectively identify fake reviews has become one of the important issues that need to be resolved. Since the existing methods do not fully consider the time burst characteristics of reviews, this paper proposes a suspicion degree determining method based on the three-dimensional time series. Besides, combining the suspicion degree feature, review text features, and reviewer's behavior features together, this paper proposes a more comprehensive fake review identification framework. The yelp and amazon public data sets are carried out to verify the effectiveness of the proposed method, and the experimental results show that the proposed method outperforms the most advanced methods.