Prediction task of drug-target interactions (DTI) is an important step of drug development and repositioning. Experimental identification of drugs and target interactions is expensive and time-consuming. Therefore, predictive drug-target interactions with computational approaches are being developed to alleviate work in drug development. In recent years, many computational approaches aimed at predicting drug-target interactions have been developed. One of the most popular models for predicting drug interactions and targets in recent times is the machine learning-based approach and homogeneous network information. However, the accuracy and efficiency of the methods used still need to be improved. Therefore, this research aims to propose a deep learning-based prediction model for DTI implemented in heterogeneous networks. We use 12,015 nodes and 1,895,445 edges that extract from several databases to build the heterogeneous network. The model of DTI prediction that we proposed implements the random walk with restart (RWR) algorithm to build a heterogeneous network of drug and protein targets, and utilizes diffusion component analysis (DCA) algorithm to obtain low-dimensional vectors. Furthermore, a one-dimensional convolutional neural network (1D-CNN) was used as a predictive model between drug and target. The results show that our proposed model provides good performance with a mean score of AUROC was 0.9332, and a mean score of AUPR was 0.9402.