In the existing scientific collaboration potential prediction methods， feature engineering is used to extract the shallow and static attributes of authors in scientific collaboration networks manually. At the same time， the relationships among heterogeneous entities in the scientific collaboration networks are ignored. To address this shortcoming， a dynamic Collaboration Potential Prediction （CPP） model was proposed to incorporate the potential attribute information of multiple entities in scientific collaboration networks. In this model， the structural features of scholar-scholar collaboration relationships were considered while extracting attributes of heterogeneous entities， and the model was optimized by the collaborative optimization method to realize the prediction of scientific collaboration potential while recommending scientific collaborators for scholars. To verify the effectiveness of the proposed model， the information of more than 500 000 papers published in the China Computer Federation （CCF）-recommended journals and the complete attribute information of related entities were collected and collated. And the temporal collaborative heterogeneous networks of different periods were constructed by the sliding window method to extract the dynamic attribute information of each entity during the evolution of the scientific collaborative network. In addition， to improve the generalization and practicality of the proposed model， the data from different periods were input to train the model randomly. Experimental results show that compared with the suboptimal model — Graph Sample and aggregate network （GraphSAGE）， CPP model improves the classification accuracy on collaborator recommendation task by 1.47 percentage points； for the cooperation potential prediction task， the test error of CPP is 1.23% lower than that of GraphSAGE. In conclusion， CPP model can recommend high-quality collaborators for scholars more accurately.