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.