Identifying compound-protein interactions (CPI) is crucial for drug screening, drug repurposing, and combination therapy studies. The performance of CPI prediction depends heavily on the features extracted from compounds and target proteins. The existing prediction methods use different feature combinations, but both molecular-based and network-based models have the problem of incomplete feature representations. Therefore, completely integrating the relevant features of CPI would be an effective way to solve the existing problem. This study proposed a novel model named MCPI, which integrated the PPI (protein-protein interaction) network, CCI (compound-compound interaction) network, and structure features of CPI to improve prediction performance. We compared our model with other existing methods for predicting CPI on public datasets. The experimental results showed that MCPI outperformed the peer methods. In addition, in response to the SARS-CoV-2 pandemic, we applied the model to search for potential inhibitors among FDA-approved drugs and validated the prediction results through the literature. This work may also provide potential guidance for drug development.