Due to the continuous upgrading and optimization of fishing technology and tools, and the diversification of fishing vessel operation methods, marine fishery resources are continuously depleted. Precise prediction of the operation methods of marine fishing vessels is helpful to realize effective supervision of fishing behavior of fishing vessels. In order to improve the prediction accuracy, when doing feature engineering, this paper uses a vector encoding scheme based on trajectory sequence, and uses text vectors to train the word2vec model to calculate the embedding features of each position. At present, the single method needs to be improved in terms of forecasting accuracy. This paper proposes a forecasting method based on Stacking model fusion in order to further improve the forecasting accuracy of marine fishing vessel operations. The experimental results show that the Stacking fusion model using the vector coding scheme based on the trajectory sequence has a greater improvement in prediction accuracy than a single model.

由于捕捞技术和工具的不断升级优化，以及渔船作业方式的多样化，海洋渔业资源不断枯竭。对海洋渔船作业方式的精准预测，有助于实现对渔船捕捞行为的有效监管。为了提高预测精度，本文在做特征工程时，采用了基于轨迹序列的向量编码方案，利用文本向量训练word2vec模型，计算每个位置的embedding特征。目前，单一方法在预测精度方面有待提高。为了进一步提高海洋渔船作业的预测精度，本文提出了一种基于Stacking模型融合的预测方法。