In this study, optical images of 7 microalgae were sampled. Based on the experience and knowledge of experts at home and abroad on identification of marine microalgae, an image data set labeled with algae names was made and data enhancement was carried out.With the help of the principles and methods of deep learning, the AlexNet model based on the structure of convolutional neural network was designed and trained.The 5-fold cross validation method was applied to ensure the stability of the model.The results showed that the average training accuracy of the model can reach 97.86%±1.63%and the average testing accuracy can reach 85.86%±0.80%. By using the parameters obtained from AlexNet model training, the reserved 280 sample images were actually tested.The average accuracy, average recall rate and average F1 Score of the 7 algal species were 83.2%,84.4% and 83.3%, respectively.It was indicated that the deep learning method is an effective way to identify marine toxic algal species.