Existing finger vein recognition methods primarily focus on extracting features from the spatial domain， while neglecting the frequency-domain representation of tubular tree-like veins and the embedded multi-scale details. To address this issue， a cascaded Finger Vein Recognition network based on Multi-Domain feature fusion （MD-FVR） was proposed. The method adopted a hierarchical cascaded network architecture， in which each stage consisted of a multi-domain feature fusion module and basic operations， enabling progressive feature extraction and enhancement. The core multi-domain feature fusion module operated as follows： firstly， structural information was enhanced through Depthwise Separable Wavelet Convolution blocks （DSWC）. Secondly， frequency-domain global features were extracted using Frequency-Domain Spatial and Coupling blocks （FDSCs） to compensate for limitations in spatial domain representation. Combined with an improved Half-Wavelet Spatial Attention block （HWSA）， the local frequency-domain details were further refined. Finally， the final enhanced feature representation was obtained by integrating multi-domain features through the Adaptive Feature Fusion module （AFF）. Experimental results on the HKPU and SDUMLA-HMT datasets showed that MD-FVR achieved recognition accuracies of 99.68% and 99.53%， with Equal Error Rates （EERs） of 0.35% and 0.41%， respectively. Compared with methods that rely solely on spatial or frequency features， MD-FVR demonstrates significant improvements in both recognition accuracy and robustness.

For the monotonicity of learning resources recommended by the collaborative filtering algorithms may lead to the difficulty of meeting the need of personalized resource acquisition of learners， a recommendation algorithm for online learning resources based on double-end neighbor fusion knowledge graph was proposed. Firstly， on the user end， the information of the entities and their neighbors between the learner’s existing knowledge nodes and the new knowledge nodes were aggregated to obtain the embedding representation of the learner in order to capture the learner’s personalized requirements. Secondly， on the project end， the neighborhood information of learning resources was used to expand the semantics and embedding representation of the learning resources. Finally， the user embedding representation and the project embedding representation were sent to the fully connected layer to obtain the interaction probability of them. To verify the effectiveness of the proposed algorithm， comparison experiments were performed using the public dataset MOOPer. Experimental results show that on this dataset， the proposed algorithm improves 1.12 percentage points and 1.31 percentage points on AUC （Area Under Curve） and accuracy respectively compared to the optimal baseline model， and achieves certain improvement on both of Precision@K and Recall@K.