The marine ecosystem is one of the most extensive and abundant ecosystems on Earth. Marine plankton is an important component, and its abundance, number of species, and dominant species are regarded as important monitoring indicators. Aiming at the problems of low accuracy and high complexity in identifying plankton based on convolutional neural networks, this study proposes a lightweight identification algorithm for plankton images based on the improved MobileNetV2. Firstly, the network layer structure is extracted by redesigning features to balance the depth and width of the network to reduce the model parameters; secondly, the lightweight coordinate attention (CA) mechanism is introduced to strengthen the attention and extraction ability of key areas; in addition, the structure of the network classifier is optimized to improve the utilization efficiency of the model parameters. The results show that the model achieves a 95.46% accuracy and 94.48% recall in 12 kinds of images. Compared with the initial MobileNetV2, the parameters and calculation amount are reduced by 72.47% and 52.09%, respectively, and the reasoning time for a single image is 6.15 ms. The model realizes the accurate identification of plankton in situ under the premise of ensuring it is lightweight. Combining time information and depth data, it is of great significance for marine ecological environment monitoring and prediction to obtain the abundance of various plankton.