Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosynthetic pigments can constrain the efficacy of fluorescence methods. This study introduces a multi-label classification model that combines a specific Excitation-Emission matric convolutional neural network (EEM-CNN) with three-dimensional (3D) fluorescence spectroscopy to detect single and mixed algal samples. Spectral data can be input directly into the model without transforming into images. Rectangular convolutional kernels and double convolutional layers are applied to enhance the extraction of balanced and comprehensive spectral features for accurate classification. A dataset comprising 3D fluorescence spectra from eight distinct algae species representing six different algal classes was obtained, preprocessed, and augmented to create input data for the classification model. The classification model was trained and validated using 4448 sets of test samples and 60 sets of test samples, resulting in an accuracy of 0.883 and an F1 score of 0.925. This model exhibited the highest recognition accuracy in both single and mixed algae samples, outperforming comparative methods such as ML-kNN and N-PLS-DA. Furthermore, the classification results were extended to three different algae species and mixed samples of skeletonema costatum to assess the impact of spectral similarity on multi-label classification performance. The developed classification models demonstrated robust performance across samples with varying concentrations and growth stages, highlighting CNN's potential as a promising tool for the precise identification of marine algae. © 2024 Elsevier B.V.