To address the problem that session-based recommendation models ignore temporal information and spatial relationships among items， leading to an inability to capture complex transition patterns among items accurately， a session-based recommendation model based on time-aware and space-enhanced dual channel Graph Neural Network （GNN） was proposed. Firstly， for the temporal channel， adaptive temporal weights were used to process the items， thereby constructing a time-aware session graph， and the users’ interest-shifting patterns were captured through a time-aware GNN. Secondly， for the spatial channel， spatial relationships among items were embedded into a Graph ATtention network （GAT）， so as to aggregate the information from the perspective of spatial graph structure. Finally， a contrastive learning strategy was introduced to enhance recommendation performance. The results of comparative experiments conducted on three publicly available datasets， Diginetica， Tmall， and Nowplaying — where the proposed model was compared with baseline models including Atten-Mixer （multi-level Attention Mixture network） and GCE-GNN （Global Context Enhanced GNN） — show that the proposed model achieves superior precision （P） and Mean Reciprocal Rank （MRR）. Compared to the suboptimal results， the proposed model has the P@10 improved by 2.09%， 24.97%， and 10.45%， respectively， and the MRR@10 improved by 2.52%， 11.60%， and 4.43%， respectively.

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.