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Forecasting governing key factors in industrial bioprocesses is crucial for ensuring stability and efficiency in production. However, the accurate prediction is challenged by the strong coupling and uncertainty characteristic of industrial bioprocess data. To capture the common dynamics and comprehensively model the interrelationships among multivariate time series in bioprocesses, this study introduces a predictive model called the causality enhanced global-local graph neural network. A global-local decomposition module is first constructed utilizing time regularization, thereby, explicitly obtaining global and local bioprocess series while preserving temporal structure. Subsequently, we construct node embedding for both the global and local series. Finally, we presents an innovative graph generation module that creates an explicit causality graph based on transfer entropy and an implicit static-dynamic graph for the downstream graph neural network, considering causal information, static and dynamic dependencies among variables. Application results based on real industrial bioprocess data demonstrate that this method has high predictive accuracy.