Abstract:Chinese governments at all levels have invested a large amount of funds in the form of scientific research projects to promote scientific and technological innovation. In order to improve the efficiency of the use of the funds, it is an important decision for the government to effectively predict the projects performanc in the future when they select projects to be funded. This paper proposes a performance prediction method for scientific research projects based on ensemble learning. By using multi-classification ensemble learning algorithms, the project performance related information hidden in the completed project data is effectively mined using web crawlers to form a project performance prediction model. Using the Project data of the National Natural Science Foundation of China, this paper evaluates the performance of the proposed model based on several measures. The performance prediction results of the model are compared with the expert evaluation results, and the results show the effectiveness of the proposed model.