赤潮作为海洋灾害,对海洋渔业、生态、经济,以及人类生产、生活造成了严重影响.一直以来,赤潮受到研究者的广泛关注,但由于它的形成机制比较复杂,使得赤潮预报极具挑战.针对赤潮预报的研究问题,本文收集了厦门海域赤潮发生前后的海洋监测数据,结合皮尔逊相关系数、散布矩阵、复相关系数方法,分析多环境因子与赤潮发生多要素的关联情况,重点采用基于深度学习的LSTM与CNN融合方法,挖掘环境因子的时序依赖,发现序列数据的局部特征,对赤潮发生进行预报.在厦门一号和厦门二号数据集中,本方法在预报未来12小时内的赤潮情况时,RMSE、MAE误差分别达到0.5218、0.5043.通过协同对比模型进一步确定赤潮发生的预报概率,在两个数据集上的最终预报准确率分别为67.58％,63.49％.本研究为赤潮的分析预报提供了探索经验,证明了将深度学习方法应用于赤潮预报的可行性.“,”As a marine disaster, red tides have a serious impact on marine fisheries, ecology, economy, human production and life. Red tides have been widely concerned by researchers for a long time. However, due to its complex formation mechanism, red tide forecasting is extremely challenging. Aiming at addressing problem of red tide forecasting, this paper collects the marine monitoring data before and after the occurrence of red tide in Xiamen sea area, and analyzes the correlation between multiple environmental factors and the red tide occurrence by combining the methods of Pearson correlation coefficient, Scatter matrix, and multiple correlation coefficient. The fusion method of LSTM and CNN based on deep learning are applied to mine the temporal dependence of environmental factors and find the local features of sequence data, then predict the occurrence of red tides. In the Xiamen No. 1 and Xiamen No. 2 datasets, the RMSE and MAE errors of this method are reaching 0.5218 and 0.5043, respectively. The forecast probability of red tide occurrence was further determined through the collaborative comparison model. The final forecast accuracy of the two datasets is 67.58％ and 63.49％, respectively. This study provides exploratory experience for the analysis and forecasting of red tides, which proves the feasibility of applying deep learning methods to red tide forecasting.