[Objective] This study proposes a new method to predict the relationship between drugs and targets to improve the prediction performance. [Methods] Firstly, we used the SNF, AVG, and MAX methods to fuse multiple semantic relationships in drug and target similarity networks, which further enriched the semantic information of the networks. Then, we constructed a bidirectional diffusion model based on the fused similarity networks and the existing drug-target interaction network to predict the drug-target relationship. [Results] Compared with mainstream forecasting models, our method's AUC value index improved by 2.2% and 12.8%. With a retrospective study, the prediction scores ranked in the top 10, 20, and 30 drug-target relationship pairs, and clues and evidence related to 3, 8, and 11 drug-target pairs could be found in the literature. The SNF had the best fusion effect and maximized the prediction. [Limitations] We did not fuse similarities in objective attributes of drugs or targets, such as the chemical structure of drugs or sequence structure similarities of targets. The cold start problem in the relationship between new drugs and new targets still needs to be solved. [Conclusions] The prediction method proposed in this study could provide some references for the research on drug repositioning and relationship prediction of other biomedical entities.