###### Abstract  
To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (_ACC_), fishing ground forecast precision (\(P\)) and recall (\(R\)) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (\(ACC=86.92\%\)), with an overall improvement of 4.39-19.48% over the machine learning models. (2) A better forecast accuracy (\(R_{2}\) of 81.82-98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (\(R_{1}\) of 47.37-96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of \(10^{\circ}\) S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades  
makes it challenging to achieve high-accuracy fishing ground forecasts for large-scale, complex and variable high-dimensional ocean data [11; 12]. At the same time, traditional models also have problems, such as being influenced by human factors, not having generalizability, having difficulty in feature conversion and experiencing insufficient fitting [13; 14]. Machine learning algorithms have effectively suppressed these problems in many classification and regression studies [15; 16]. However, in the modeling process, it is difficult for machine learning models to achieve high-accuracy forecasting of the distribution of different grades of fishing grounds due to the complexity and variability of marine environmental factors and the limitations of single-model algorithms, such as poor stability, easy overfitting and poor generalization ability [17; 18; 19]. While ensemble learning can effectively solve these problems [20; 21], ensemble learning algorithms produce more stable results by integrating the advantages of multiple base models and using multiple learners to solve problems together, which can effectively suppress model overfitting and improve model prediction [22; 23; 24; 25]. For example, [PERSON] et al. (2020) selected seven different machine learning algorithms for integrated learning to predict the habitat suitability of _Hemiculture leucisulus_ and concluded that the ensemble model had the highest predictive power (AUC = 0.972) [26]. [PERSON] et al. (2021) conducted a polling integration based on three single models, and their study concluded that it outperformed a single machine learning model (\(R^{2}\) = 0.75) in predicting Malaysian marine fish production [9]. However, the application effect of the ensemble learning model on different grades of albacore fishing grounds in the South Pacific has not been verified. The external environmental factors of marine fish survival are the main factors affecting the abundance and distribution of fishery resources [27; 28]. It has been proved that the spatial and temporal distribution of fishing grounds, the living habits of marine fishes and marine environmental factors are very closely related [29; 30]. [PERSON] et al.