Crowdfunding is an emerging finance platform for creators to fund their efforts by soliciting relatively small contributions from a large number of individuals using the Internet. Due to the unique rules, a campaign succeeds in trading only when it collects adequate funds in a given time. To prevent creators and backers from wasting time and efforts on failing campaigns, dynamically estimating the success probability of a campaign is very important. However, existing crowdfunding systems neither have the mechanism of dynamic predictive tracking, nor consider the dynamic interaction between project sponsors and investors on the platform. To address these issues, a novel dynamic and static collaborative prediction model is designed based on long and short-term memory network. This model focuses on user behavior, including the emotional tendency of reviews and the dynamic incremental information in the financing process, so as to deeply mine and analyze the interaction between financing projects and investors. Firstly, for the static features and dynamic user behavior data on the platform, their deep characterization is obtained by different embedding methods. On this basis, a collaborative prediction model based on attention mechanism is further designed to understand the impact of timing information of project financing on the final results. Finally, experiments on real crowdfunding datasets show that the proposed dynamic and static representation prediction method is more effective than other prediction methods.