When dealing with multivariate time series, traditional prediction models are often difficult to capture the complex variation of nonlinear dynamic systems, which results in low prediction accuracy. To solve this problem, this study discusses and verifies the prediction method of the PCC-BiLSTM-GRU-Attention combined model. In the method, Pearson correlation coefficient (PCC) is first used for correlation tests and irrelevant features are deleted to achieve dimensionality reduction and optimization of multivariate data. Then, bidirectional long short-term memory (BiLSTM) neural network is used to extract time series features. Finally, GRU neural network is integrated with the attention mechanism to further learn the change rule of bidirectional time series features and accurately capture the critical moment information. To verify the feasibility of this method in multivariate time series, this study takes stock price prediction as the experimental scene and compares it with the BP model, LSTM model, GRU model, BiLSTM-GRU model and BiLSTM-GRU-Attention model. The verification results show that the prediction method of the PCC-BiLSTM-GRU-Attention combined model has higher prediction accuracy than other models, with the mean absolute percentage error (MAPE) reaching 2.484% and the determination coefficient 0.966.