To address the issues of existing multi-source data representation learning models in processing large-scale， complex， and high-dimensional data， specifically the tendency to overlook high-order association among different sources， and susceptibility to noise， a Multi-Source data representation learning model based on Tensorized Graph convolutional network and Contrastive learning， namely MS-TGC， was proposed. Firstly， the K-Nearest Neighbors （KNN） algorithm and Graph Convolutional Network （GCN） were used to unify multi-source data dimensions， forming tensorized multi-source data. Then， a defined tensor graph convolution operator was applied to perform high-dimensional graph convolution operations， enabling simultaneous learning of intra-source and inter-source information. Finally， a multi-source contrastive learning paradigm was constructed to enhance the accuracy of representation learning in noisy data and improve robustness against noise by incorporating contrastive constraints based on semantic consistency and label consistency. Experimental results show that when the labeled sample ratio is 0.3， MS-TGC achieves 1.36 and 5.53 percentage points higher semi-supervised classification accuracy than CONMF （Co-consensus Orthogonal Non-negative Matrix Factorization） on BDGP and 20newsgroup datasets， respectively. These results indicate that MS-TGC effectively captures inter-source correlations， reduces noise interference， and achieves high-quality multi-source data representations.