Aiming at the problem that it is difficult to capture the temporal information during the dynamic change of video popularity, a Deep Attention video popularity prediction model Fusing Content and Temporal information (DAFCT) was proposed. Firstly, according to the users' feedback information, an Attention mechanism based Long Short-Term Memory network (Attention-LSTM) model was constructed to capture the popular trend and mine the temporal information. Secondly, Neural Factorization Machine (NFM) was used to process multi-modal content features and embedding techniques were adopted to reduce the computational complexity of the model by reducing the dimension of sparse high-dimensional features. Finally, the concatenate method was employed to fuse the temporal information and content features, and a Deep Attention Video Popularity Prediction (DAVPP) algorithm was designed to solve the proposed DAFCT. Experimental results show that compared with Attention-LSTM model and NFM model, the recall of DAFCT is improved by 10.82 and 3.31 percentage points, and the F1 score was improved by 9.80 and 3.07 percentage points, respectively.