Vehicle trajectory prediction is one of the key tasks in autonomous driving technology. Accurate prediction of vehicle trajectories is of great significance for driving safety and traffic efficiency in intelligent connected environments. To address the problem of vehicle trajectory prediction in complex interactive scenarios, this paper proposes a trajectory prediction network that integrates spatio-temporal correlation embedding relationships. The network consists of three components: spatio-temporal correlation embedding, fused feature extraction, and trajectory prediction. First, the spatio-temporal correlation embedding module employs a dual-layer convolutional structure to integrate local trajectory temporal information with spatial relationships. Second, a Rotary Positional Embedding (RoPE) mechanism is introduced into the encoder to capture global spatio-temporal dependencies in long-term trajectories. Finally, a multi-modal decoder is utilized for parallel trajectory prediction to reduce cumulative prediction errors. In terms of feature representation, ego-vehicle features, relative relationship features, and traffic environment features are employed to enhance scene representation capabilities. Experiments are conducted based on the NGSIM dataset. The results demonstrate that the proposed method outperforms LSTM, TCN, Transformer, and other methods, achieving a reduction of 0.296 meters in average final displacement error.