ing at the identification of the characteristics of the discrete three-dimensional fluorescence spectra for the planktonic mixed algae community, the spiecies identification accuracy and concentration measurement accuracy of mixed data of five common phylum species of algae (Microcystis aeruginosa, Scenedesmus obliquus, Nitzschia sp., Peridinium umbonatum var.inaequale and Cryptomonas obovata.) are compared and analyzed by the plain convolutional neural network (PlainCNN) model and the text convolutional neural network (TextCNN) model. The results show that in the algae independent identification and concentration regression analysis, the average identification accuracy of the test set and the average mean square error of the results of the concentration output of the PlainCNN model are 90% and 0.052 respectively, which are better than that of TextCNN model. In order to realize species identification and concentration analysis of mixed algae at the same time, a multi-task convolutional neural network, i.e., PlainCNN-MT model, is proposed based on the PlainCNN model. The average accuracy of the model for the species identification of mixed algae is increased to 95%, and the average mean square error of the results of the concentration output is reduced to 0.039, indicating that the multi-task convolutional neural network has more advantages in the identification and quantitative analysis of planktonic algae community.