Most studies in drug repositioning field rely on the “similar drugs treat similar diseases” hypothesis， requiring similarity data between diseases and drugs. However， such data has acquisition difficulties， different methods have significant discrepancies in computational results， and the research has inability to conduct when data is missing. To address these issues， this study proposed a drug repositioning method， EMP-PCA， based on Empirical Meta-Path （EMP） and Principal Component Analysis （PCA） that achieved drug-disease association prediction without similarity data. First， five meta-paths corresponding to different interaction data were introduced to generate a commuting matrix for mining multi-source correlation information. Second， PCA was employed to identify the directions with the highest variances， dimensionality reduction was performed through data projection， and computations were simplified while retaining core information. Finally， Gradient Boosting Tree （GBT） method was used to construct base classifiers for various meta-paths， and the classifiers were combined into an ensemble classifier to integrate multi-source data effectively. Experimental results of comparing EMP-PCA with similarity data-based methods like Drug Repurposing via Heterogeneous Graph Convolutional Network （DRHGCN）， Additional Neural Matrix Factorization （ANMF） model， and Layer-wise Attention Graph Convolutional Network （LAGCN） demonstrate that EMP-PCA can fuse multi-source interaction data among drugs， proteins， and diseases without requiring similarity data effectively， and the method outperforms the comparison methods in key evaluation metrics including Area Under Curve （AUC）， precision， and recall， resolving data dependency and missing value issues inherent in similarity-based methods effectively， and has high practical application value.