The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential DTIs. The performance of DTI prediction depends heavily on the features extracted from drugs and target proteins. The existing predictors vary in input information and each has its own advantages. Therefore, combining the advantages of individual models and generating high-quality representations for drug-target pairs are effective ways to improve the performance of DTI prediction. In this study, we exploit both biochemical characteristics of drugs via network integration and molecular sequences via word embeddings, then we develop an ensemble model, KenDTI, based on two types of methods, i.e., network-based and classification-based. We assess the performance of KenDTI on two large-scale datasets, The experimental results show that KenDTI outperforms the state-of-the-art DTI predictors by a large margin. Moreover, KenDTI is robust against missing data in input networks and lack of prior knowledge. It is able to predict for drug-candidate chemical compounds with scarce information.