DTI prediction serves as a vital function in drug discovery and repositioning. In this paper, we propose a novel method, DFDGRU-DTI, which employs a random-walk–based embedding approach combined with a BiGRU neural network augmented by multi-head attention to predict drug–target interactions. First, the random walk embedding model is used to generate feature vectors for drugs and targets, capturing the semantic relationships between them. Then, a bidirectional GRU network is employed for sequence learning, and the inclusion of multi-head attention helps the model concentrate on the most relevant input features. The evaluation demonstrates that the introduced model delivers superior performance relative to numerous advanced drug–target interaction prediction techniques. This model exhibits significant application potential, providing valuable support for real-world drug development.