Abstract: OBJECTIVE To build an efficient drug-target prediction classification model and provide a useful complementary tool for biological experiments. METHODS In this study, a deep learning-based method was developed to predict drug target interaction. By introducing high dimensional molecular fingerprints and protein descriptors, and subsequently applying a probability matrix decomposition algorithm to generate negative samples, a promising drug target interaction classification model was constructed. RESULTS The method was comparable or superior to previously developed methods against the test sets, achieving ˃90% accuracy, specificity, sensitivity, and AUC. This method represented a promising tool for drug target prediction. CONCLUSION The combination of artificial intelligence deep learning model and probabilistic matrix factorization algorithm can help to solve the problems of low prediction accuracy of drug-target interaction and unreasonable selection of negative samples.