Abstract: Sap flow is one of the important physiological parameters of moso bamboo, reflecting the plant's utilization of water and its response to the environment. To explore the impact of environmental factors on moso bamboo sap flow and the relationship between sap flow density and key environmental variables, this study employs bamboo forest environmental parameters and deep learning methods to model and predict moso bamboo sap flow. The data used for analysis includes measurements of soil moisture, soil temperature, air humidity, air temperature, CO₂ concentration, light intensity, and moso bamboo sap flow density, which were all collected synchronously. Initially, the study applies grey relational analysis to evaluate the correlation between multi-dimensional environmental time-series signals and the sap flow signal. This allows for identifying the degree to which different environmental factors influence the sap flow. Subsequently, the study introduces a novel combined deep learning model called FCN-GRU-TPA for modeling and prediction. The FCN-GRU-TPA model is designed to capture temporal dependencies, select relevant cross-step length information, and perform parallel computation. These advantages theoretically lead to higher prediction accuracy compared to traditional models. Finally, the robustness and generalization capability of the model are evaluated through four experimental modes, incorporating data from various time periods and multiple moso bamboo plants. The experimental results demonstrate that environmental factors such as air temperature and humidity, soil temperature and humidity, light intensity, and CO₂ concentration are correlated with moso bamboo sap flow to varying degrees. The strongest correlation is observed with light intensity, followed by soil temperature and air temperature. When all six environmental factors are used as input variables, the model provides a highly accurate prediction of sap flow density. However, when only three of the most strongly correlated environmental factors are selected as inputs, the performance of the prediction slightly decreases, although the results still remain reasonable. The study further tests the model using two different ratios of modeling and prediction data—80:20 and 50:50. The 50:50 ratio produces acceptable results when all six parameters are used in the model, but the performance tends to decline when only three parameters are used for modeling. Across all four experimental modes, the average normalized Root Mean Square Error (NRMSE) is found to be less than 4.00%, and the coefficient of determination (R²) exceeds 0.90, indicating a high level of prediction accuracy. Moreover, the consistency of the model’s performance across different moso bamboo plants and various time periods further supports the reliability and robustness of the approach. The model shows that, while different environmental factors influence moso bamboo sap flow, the relationships between these factors and the sap flow are relatively stable and can be modeled effectively using the deep learning-based FCN-GRU-TPA framework. This study demonstrates that applying grey relational analysis along with the FCN-GRU-TPA model can effectively establish a predictive model for moso bamboo sap flow based on multiple environmental factors. The model not only achieves high prediction accuracy but also shows strong robustness and generalization capability, meaning it can adapt to variations in the input data and still deliver reliable results. Additionally, the model’s flexibility allows for the adjustment of the number of input variables and training sample sizes without compromising performance, making it adaptable to different experimental settings and data availability. The findings from this research provide valuable insights for further studies on the relationship between environmental variables and plant physiology, and they offer a useful reference for predicting the physiological parameters and growth behaviors of other plant species in different environmental contexts. Overall, the methods and results from this study contribute significantly to advancing the understanding of plant-water relationships and can be applied to a range of ecological and agricultural studies focused on improving plant growth models and resource management.