With the development of online social networks, more and more people use Twitter or Weibo to propagate information or share opinions. Predicting the diffusion of information on social networks is a key problem for applications like opinion leader detection, public opinion monitoring or viral marketing. Although there has been extensive research on information diffusion model, what the factors that influence information diffusion are and how to characterize the process of information diffusion on social networks are still open problems. Traditional models pay more attention on network structures, and largely ignore important dimensions such as user attributes or information content features. In this paper, we extract features from multiple dimensions including node attributes and information contents, proposing a fine-grained information diffusion model based on these features. We use stochastic gradient descent method to learn the weights of these features in the proposed model, and then give quantitative analysis. Our model gives an insight into the extent on which these features can influence information diffusion on social network. Besides, given an initial state of information diffusion, our model can be used to predict the future diffusion process. Experiment results on Sina Weibo datasets show that the proposed model yields higher prediction precision than other widely used models like AsIC model or NetRate model.