## 1 Introduction  
Gross primary productivity (GPP) is the cumulative sum of organisms produced by plants absorbing CO\({}_{2}\) during photosynthesis [1, 2]. It drives the seasonal and annual variations in atmospheric CO\({}_{2}\) concentration, which reflect the production capacity of terrestrial ecosystems under natural conditions [2, 3], and is an important indicator for assessing ecosystem health and climate change [4]. Therefore, accurate prediction of GPP is crucial for ecosystem function evaluation and carbon balance research [2].  
The common methods for quantifying and predicting GPP are based on processing observational data and process-based model simulation [2, 5]. Observational data include data obtained using the eddy covariance (EC) technique, in which GPP values are obtained by calculating net ecosystem exchange (NEE) from vertical turbulent transport in the atmosphere under meteorological conditions [5, 6], and satellite data, which are commonly used for GPP estimation due to their stability and sustainability, such as the MODIS GPP standard product, the VPM model, and the EC-LUE model [2, 7, 8, 9]. However, satellite GPP products cannot fully guarantee the reliability of data [10], which affects the accuracy of the prediction data and introduces uncertainties to related research. Process-based models mainly investigate and simulate ecological processes occurring in plants and have extensive theoretical foundations in related fields. However, process-based models have complex structures and often simulate ideal ecological processes that deviate from actual conditions [2, 11], which affects model accuracy. In addition, plant organisms involve complex and nonlinear biological and chemical mechanisms [2, 12, 13], which pose a great challenge for process-based models to simulate these mechanisms.  
Currently, artificial intelligence (AI) algorithms have been widely applied in various fields because they can fit complex nonlinear mapping relationships between predictive and driving factors without requiring as many complicated prior assumptions as traditional models do [14]. Commonly applied machine learning models include Random Forest (RF), Support Vector Machine (SVM), and neural networks such as Long-Short Term Memory (LSTM). [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [17], and other researchers used AI methods for tree species classification and carbon flux prediction, demonstrating the potential of AI in ecology. In recent years, [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [4], and others used RF, Convolutional Neural Network (CNN), and Deep Belief Network (DBN) to predict GPP and achieved good results.  
In this work, we constructed a model with spatial-temporal correlation while considering environmental factor fusion based on the GeoMAN model, a network with a multi-level attention mechanism developed by [PERSON] et al. [21]. We trained and parameterized the algorithm with observational data on GPP from various sites and environmental driving data to extract nonlinear mapping relationships between GPP and multiple environmental factors. We designed a series of case studies to assess the performance of this deep learning model, which was based on the attention mechanism, by examining the impacts of distance, vegetation, and environmental factors on the prediction results across various flux sites. Compared with the previous applications of AI models in cross-disciplinary fields, our method not only fully utilizes the high precision of AI in prediction but also considers the prior knowledge within the relevant field to ensure that the results are both more accurate and consistent with domain knowledge.