The field-aware factorization machine model(FFM) is widely used in the field of recommender systems since it can effectively solve the sparse problem of high dimensional feature combination with high prediction accuracy and computation efficiency. However, the FFM does not consider time dynamics in the modeling phase. In real scene, some feature values change with time and they will have different effects on prediction at different time. A field-aware factorization machine model based on the time dynamics FFM(tFFM) is proposed. The model takes into account two kinds of time dynamics, bias dynamics and feature dynamics. The former is modelled dynamically from user behavior changes and popularity of items respectively, and different time granularities based on time-window technique are used. The latter subdivides features into static features that maintain stability and dynamic features that change with time, and the time function is established by using the ReLU activation function. The unified feature encoding method is used and a sample data representation and access strategy is designed to greatly reduce the time complexity of model training and prediction. The stochastic optimization method Adam is used to optimize the target. The experimental results show that the tFFM can obtain higher prediction accuracy compared with the state-of-the-art methods related to factorization machines such as the FM and the FFM.