Time series is a kind of high-dimensional data that exists widely in various fields in reality, and time series prediction is a research focus in research activities related to time series. The traditional time series prediction methods only analyze the time series from the time dimension, ignoring the influence of external influence factors on the time series. Aiming at the problems existing in traditional methods, this paper proposes a time series prediction model based on deep learning named dual-stage attention and full dimension convolution based recurrent neural network(DAFDC-RNN). The model introduces a target attenion mechanism to learn the correlation between the input features and the predicted features, introduces a full dimension convolution mechanism to learn the correlation among input features, and introduces a temporal attention mechanism to learn the long-term temporal dependencies of time series. In the experimental part, we firstly determine the hyperparameters of the model, and then verify the validity of the model's components. Finally, the comparative experiments show that the proposed DAFDC-RNN model has the best prediction effect on the dataset with large features.